Online R learning for applied statistics

:bar_chart: Methods of Applied Statistics Course Textbook Repository

Materials for STATS 418 - Tools in Data Science course taught in the Master of Applied Statistics at UCLA

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http://www.seedpeer.me/details/6571637/TutsPlus---Modern-Testing-in-PHP-with-Codeception.html http://www.seedpeer.me/details/6095651/Tutsplus---Parallax-Scrolling-for-Web-Design.html http://www.seedpeer.me/details/6574591/TutsPlus---Say-Yo-to-Yeoman.html http://www.seedpeer.me/details/4811335/Tutsplus---Test-Driven-Development-in-Ruby.html http://www.seedpeer.me/details/6268980/TutsPlus-Test-Driven-Development-With-CoffeeScript-and-Jasmine.html http://www.seedpeer.me/details/6185755/TutsPlus---The-MVC-Mindser-Jeffery-Way---ICARUS.html http://www.seedpeer.me/details/5024493/TutsPlus---Venture-Into-Vim.html http://www.seedpeer.me/details/6286416/Tutsplus---Vim-for-Advanced-Users.html http://www.seedpeer.me/details/6585031/Tutsplus---WordPress-Hackers-Guide-to-the-Galaxy.html http://www.seedpeer.me/details/4848477/TutsPlus---Writing-Modular-JavaScript.html @giansalex Owner Author giansalex commented on 26 Feb 2018 • SOLID http://www.allitebooks.com/beginning-solid-principles-and-design-patterns-for-asp-net-developers/ @giansalex Owner Author giansalex commented on 7 Mar 2018 Udemy: AWS Arquitecto de Soluciones Certificado Asociado https://mega.co.nz/#!ZzhGWSAL!wuthFca0SdJBjmaP5lFX0QF6PeMsrdclKFXlZL1Rsi4 Pass: gratismas.org @giansalex Owner Author giansalex commented on 7 Mar 2018 Go lang Complete https://www.freetutorials.us/wp-content/uploads/2017/11/FreeTutorials.Us-Udemy-go-the-complete-developers-guide.torrent @GCPBigData GCPBigData commented on 15 Jul 2018 go books https://drive.google.com/open?id=1d6OsFAn8kpHCXtw0bcoYuyHqrAdGZva0 @freisrael freisrael commented on 14 Aug 2018 giansalex thanks for sharing. I am looking for learning phython with Joe Marini. It would be great if you post it. @FirstBoy1 FirstBoy1 commented on 25 May 2019 Can anyone provide this book "Getting started with Spring Framework: covers Spring 5" by " J Sharma (Author), Ashish Sarin ". Thanks in advance @okreka okreka commented on 31 May 2019 Can anyone provide "Windows Presentation Foundation Masterclass" course from Udemy. Thanks in advance @singhaltanvi singhaltanvi commented on 8 Aug 2019 can anyone provide 'sedimentology and petroleum geology' course from Udemy. Thanks in advance. @kumarsreenivas051 kumarsreenivas051 commented on 9 Sep 2019 Can anyone provide "Programming languages A,B and C" course from Coursera. Thanks in advance. @BrunoMoreno BrunoMoreno commented on 11 Sep 2019 The link for the torrents in piratebay, now is .org to the correct url. @sany2k8 sany2k8 commented on 24 Sep 2019 Can anyone add this The Complete Hands-On Course to Master Apache Airflow @pharaoh1 pharaoh1 commented on 30 Sep 2019 can you pls add this course to your list https://www.udemy.com/course/advanced-python3/ @SushantDhote936 SushantDhote936 commented on 1 Oct 2019 Can you add Plural Sight CISSP @allayGerald allayGerald commented on 1 Oct 2019 open directive for lynda courses: https://drive.google.com/drive/folders/1zQan1cq1ZnqXmueRF5IqKoOtpFxl6Y4G @ezekielskottarathil ezekielskottarathil commented on 3 Oct 2019 can anyone provide 'sedimentology and petroleum geology' course from Udemy. Thanks in advance. "wrong place boy" @pulkitd2699 pulkitd2699 commented on 8 Oct 2019 Does anyone has a link for 'Cyber security: Python and web applications' course? Thanks @mohanrajrc mohanrajrc commented on 19 Oct 2019 • Can anyone provide torrent file for Mastering React By Mosh Hamedani. Thanks https://codewithmosh.com/p/mastering-react @evilprince2009 evilprince2009 commented on 27 Oct 2019 Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 @nunusandio nunusandio commented on 30 Oct 2019 Can anyone post torrent file for ASP.NET Authentication: The Big Picture https://app.pluralsight.com/library/courses/aspdotnet-authentication-big-picture/table-of-contents @EslamElmadny EslamElmadny commented on 9 Dec 2019 Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? @Genius-K-SL Genius-K-SL commented on 14 Dec 2019 hay brother! do you have html5 game development with javascript course ? @Genius-K-SL Genius-K-SL commented on 14 Dec 2019 This link is not working brother! http://www.seedpeer.me/details/4657790/Lynda.com-Building-Facebook-Applications-with-HTML-and-JavaScript.html @smithtuka smithtuka commented on 20 Dec 2019 Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? @AbdOoSaed AbdOoSaed commented on 22 Dec 2019 Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff @EslamElmadny EslamElmadny commented on 23 Dec 2019 • Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj @jedi2610 jedi2610 commented on 27 Dec 2019 Can anyone provide me with Code with Mosh's Ultimate Java Mastery Series link? plis @InnocentZaib InnocentZaib commented on 31 Dec 2019 Please provide the link of codewithmosh The ultimate data structures and algorithms Bundle the link is given below. Please give me the torrnet file or link to download https://codewithmosh.com/p/data-structures-algorithms @edward-teixeira edward-teixeira commented on 1 Jan 2020 Please provide the link of codewithmosh The ultimate data structures and algorithms Bundle the link is given below. Please give me the torrnet file or link to download https://codewithmosh.com/p/data-structures-algorithms Yea i'm looking for it too @kaneyxx kaneyxx commented on 1 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj could you please share the part-1 & part-3? @edward-teixeira edward-teixeira commented on 2 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj Can you share part 1 and 3? @ravisharmaa ravisharmaa commented on 7 Jan Please add this . https://www.letsbuildthatapp.com/course/AppStore-JSON-APIs @WaleedAlrashed WaleedAlrashed commented on 13 Jan This one kindly. https://www.udemy.com/course/flutter-build-a-complex-android-and-ios-apps-using-firestore/ @Sopheakmorm Sopheakmorm commented on 19 Jan Anyone have this course: https://www.udemy.com/course/mcsa-web-application-practice-test70-480-70-483-70-486 @EslamElmadny EslamElmadny commented on 19 Jan Anyone have this course: https://www.udemy.com/course/mcsa-web-application-practice-test70-480-70-483-70-486 +1 @EslamElmadny EslamElmadny commented on 20 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj Can you share part 1 and 3? https://vminhsang.name.vn/category/it-courses/codewithmosh/ this link includes almost all mosh courses @mohanrajrc mohanrajrc commented on 22 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj Can you share part 1 and 3? https://vminhsang.name.vn/category/it-courses/codewithmosh/ this link includes almost all mosh courses Yes. Java mastery and Data Structures 1, 2, 3 are available in this site. free download. @shihab122 shihab122 commented on 22 Jan Please give me the torrnet file or link to download The Ultimate Design Patterns @EslamElmadny EslamElmadny commented on 22 Jan • Please give me the torrnet file or link to download The Ultimate Design Patterns Waiting for it also :D @K-wachira K-wachira commented on 23 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj Can you share part 1 and 3? https://vminhsang.name.vn/category/it-courses/codewithmosh/ this link includes almost all mosh courses Yes. Java mastery and Data Structures 1, 2, 3 are available in this site. free download. You are a saviour .. Altho i feel bad i cant really buy the course... its really good @msdyn95 msdyn95 commented 25 days ago • Please give me the torrent file or link to download https://codewithmosh.com/p/design-patterns https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-1/ https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-2/ @K-wachira K-wachira commented 23 days ago This one kindly. https://www.udemy.com/course/flutter-build-a-complex-android-and-ios-apps-using-firestore/ Hey did you find this one? @edward-teixeira edward-teixeira commented 22 days ago Please give me the torrent file or link to download https://codewithmosh.com/p/design-patterns https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-1/ https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-2/ Did you find those? @msdyn95 msdyn95 commented 21 days ago Please give me the torrent file or link to download https://codewithmosh.com/p/design-patterns https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-1/ https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-2/ Did you find those? unfortunately not. @edward-teixeira edward-teixeira commented 20 days ago Please give me the torrent file or link to download https://codewithmosh.com/p/design-patterns https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-1/ https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-2/ Did you find those? unfortunately not. Found it ! https://vminhsang.name.vn/category/it-courses/codewithmosh/ @ZainA14 ZainA14 commented 16 days ago • Can someone please link me to this mosh course for torrent or direct download link https://codewithmosh.com/p/the-ultimate-full-stack-net-developer-bundle @khushiigupta khushiigupta commented 9 days ago Can any one please provide me link for jenkins so that I can learn as al as possible to join this conversation on GitHub. Already have an account? Sign in to comment © 2020 GitHub, Inc. Terms Privacy Security Status Help Contact GitHub Pricing API Training Blog About

An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68] Decision trees Main article: Decision tree learning Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making. Support vector machines Main article: Support vector machines Support vector machines (SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.[69] An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Illustration of linear regression on a data set. Regression analysis Main article: Regression analysis Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization (mathematics) methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel[70]), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher-dimensional space. Bayesian networks Main article: Bayesian network A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet. A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. Genetic algorithms Main article: Genetic algorithm A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.[71][72] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[73] Training models Usually, machine learning models require a lot of data in order for them to perform well. Usually, when training a machine learning model, one needs to collect a large, representative sample of data from a training set. Data from the training set can be as varied as a corpus of text, a collection of images, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Federated learning Main article: Federated learning Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.[74] Applications There are many applications for machine learning, including: Agriculture Anatomy Adaptive websites Affective computing Banking Bioinformatics Brain–machine interfaces Cheminformatics Citizen science Computer networks Computer vision Credit-card fraud detection Data quality DNA sequence classification Economics Financial market analysis[75] General game playing Handwriting recognition Information retrieval Insurance Internet fraud detection Linguistics Machine learning control Machine perception Machine translation Marketing Medical diagnosis Natural language processing Natural language understanding Online advertising Optimization Recommender systems Robot locomotion Search engines Sentiment analysis Sequence mining Software engineering Speech recognition Structural health monitoring Syntactic pattern recognition Telecommunication Theorem proving Time series forecasting User behavior analytics In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million.[76] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.[77] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.[78] In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors' jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[79] In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists.[80] In 2019 Springer Nature published the first research book created using machine learning.[81] Limitations Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.[82][83][84] Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[85] In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.[86] Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested.[87][88] Bias Main article: Algorithmic bias Machine learning approaches in particular can suffer from different data biases. A machine learning system trained on current customers only may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the same constitutional and unconscious biases already present in society.[89] Language models learned from data have been shown to contain human-like biases.[90][91] Machine learning systems used for criminal risk assessment have been found to be biased against black people.[92][93] In 2015, Google photos would often tag black people as gorillas,[94] and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all.[95] Similar issues with recognizing non-white people have been found in many other systems.[96] In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.[97] Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.[98] Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There’s nothing artificial about AI...It’s inspired by people, it’s created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.”[99] Model assessments Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.[100] In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The total operating characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used receiver operating characteristic (ROC) and ROC's associated area under the curve (AUC).[101] Ethics Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[102] For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.[103][104] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases.[105][106] Other forms of ethical challenges, not related to personal biases, are more seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these "greed" biases are addressed.[107] Hardware Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units.[108] By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI.[109] OpenAI estimated the hardware compute used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.[110][111] Software Software suites containing a variety of machine learning algorithms include the following: Free and open-source so

NICEHASH PLATFORM TERMS OF USE AND NICEHASH MINING TERMS OF SERVICE PLEASE READ THESE NICEHASH PLATFORM TERMS OF USE AND NICEHASH MINING TERMS OF SERVICE (“Terms”) CAREFULLY BEFORE USING THE THE PLATFORM OR SERVICES DESCRIBED HEREIN. BY SELECTING “I AGREE”, ACCESSING THE PLATFORM, USING NICEHASH MINING SERVICES OR DOWNLOADING OR USING NICEHASH MINING SOFTWARE, YOU ARE ACKNOWLEDGING THAT YOU HAVE READ THESE TERMS, AS AMENDED FROM TIME TO TIME, AND YOU ARE AGREEING TO BE BOUND BY THEM. IF YOU DO NOT AGREE TO THESE TERMS, OR ANY SUBSEQUENT AMENDMENTS, CHANGES OR UPDATES, DO NOT ACCESS THE PLATFORM, USE NICEHASH MINING SERVICES OR USE THE NICEHASH MINING SOFTWARE. GENERAL These Terms apply to users of the NiceHash Platform (“Platform” and NiceHash Mining Services (“Services”) which are provided to you by NICEHASH Ltd, company organized and existing under the laws of the British Virgin Islands, with registered address at Intershore Chambers, Road Town, Tortola, British Virgin Islands, registration number: 2048669, hereinafter referred to as “NiceHash, as well as “we” or “us”. ELIGIBILITY By using the NiceHash platform and NiceHash Mining Services, you represent and warrant that you: are at least Minimum Age and have capacity to form a binding contract; 
 have not previously been suspended or removed from the NiceHash Platform;
 have full power and authority to enter into this agreement and in doing so will not violate any other agreement to which you are a party;
 are not not furthering, performing, undertaking, engaging in, aiding, or abetting any unlawful activity through your relationship with us, through your use of NiceHash Platform or use of NiceHash Mining Services;
 will not use NiceHash Platform or NiceHash Mining Services if any applicable laws in your country prohibit you from doing so in accordance with these Terms.
 We reserve the right to terminate your access to the NiceHash Platform and Mining Services for any reason and in our sole and absolute discretion. Use of NiceHash Platform and Mining Services is void where prohibited by applicable law. Depending on your country of residence or incorporation or registered office, you may not be able to use all the functions of the NiceHash Platform or services provided therein. It is your responsibility to follow the rules and laws in your country of residence and/or country from which you access the NiceHash Platform. DEFINITIONS NiceHash Platform means a website located on the following web address: www.nicehash.com. NiceHash Mining Services mean all services provided by NiceHash, namely the provision of the NiceHash Platform, NiceHash Hashing power marketplace, NiceHash API, NiceHash OS, NiceHash Mining Software including licence for NiceHash Miner, NiceHash Private Endpoint, NiceHash Account, NiceHash mobile apps, and all other software products, applications and services associated with these products, except for the provision of NiceHash Exchange Services. NiceHash Exchange Service means a service which allows trading of digital assets in the form of digital tokens or cryptographic currency for our users by offering them a trading venue, helping them find a trading counterparty and providing the means for transaction execution. NiceHash Exchange Services are provided by NICEX Ltd and accessible at the NiceHash Platform under NiceHash Exchange Terms of Service. Hashing power marketplace means an infrastructure provided by the NiceHash which enables the Hashing power providers to point their rigs towards NiceHash stratum servers where Hashing power provided by different Hashing power providers is gathered and sold as generic Hashing power to the Hashing power buyers. Hashing power buyer means a legal entity or individual who buys the gathered and generic hashing power on the Hashing power marketplace from undefined Hashing power providers. Hashing power provider means a legal entity or individual who sells his hashing power on the Hashing power marketplace to undefined Hashing power buyers. NiceHash Mining Software means NiceHash Miner and any other software available via the NiceHash Platform. NiceHash Miner means a comprehensive software with graphical user interface and web interface, owned by NiceHash. NiceHash Miner is a process manager software which enables the Hashing power providers to point their rigs towards NiceHash stratum servers and sell their hashing power to the Hashing power buyers. NiceHash Miner also means any and all of its code, compilations, updates, upgrades, modifications, error corrections, patches and bug fixes and similar. NiceHash Miner does not mean third party software compatible with NiceHash Miner (Third Party Plugins and Miners). NiceHash QuickMiner means a software accessible at https://www.nicehash.com/quick-miner which enables Hashing power providers to point their PCs or rigs towards NiceHash stratum servers and sell their hashing power to the Hashing power buyers. NiceHash QuickMiner is intended as a tryout tool. Hashing power rig means all hardware which produces hashing power that represents computation power which is required to calculate the hash function of different type of cryptocurrency. Secondary account is an account managed by third party from which the Account holder deposits funds to his NiceHash Wallet or/and to which the Account holder withdraws funds from his NiceHash Wallet. Stratum is a lightweight mining protocol: https://slushpool.com/help/manual/stratum-protocol. NiceHash Account means an online account available on the NiceHash Platform and created by completing the registration procedure on the NiceHash Platform. Account holder means an individual or legal entity who completes the registration procedure and successfully creates the NiceHash Account. Minimum Age means 18 years old or older, if in order for NiceHash to lawfully provide the Services to you without parental consent (including using your personal data). NiceHash Wallet means a wallet created automatically for the Account holder and provided by the NiceHash Wallet provider. NiceHash does not hold funds on behalf of the Account holder but only transfers Account holder’s requests regarding the NiceHash Wallet transaction to the NiceHash Wallet provider who executes the requested transactions. In this respect NiceHash only processes and performs administrative services related to the payments regarding the NiceHash Mining Services and NiceHash Exchange Services, if applicable. NiceHash Wallet provider is a third party which on the behalf of the Account holder provides and manages the NiceHash Wallet, holds, stores and transfers funds and hosts NiceHash Wallet. For more information about the NiceHash Wallet provider, see the following website: https://www.bitgo.com/. Blockchain network is a distributed database that is used to maintain a continuously growing list of records, called blocks. Force Majeure Event means any governmental or relevant regulatory regulations, acts of God, war, riot, civil commotion, fire, flood, or any disaster or an industrial dispute of workers unrelated to you or NiceHash. Any act, event, omission, happening or non-happening will only be considered Force Majeure if it is not attributable to the wilful act, neglect or failure to take reasonable precautions of the affected party, its agents, employees, consultants, contractors and sub-contractors. SALE AND PURCHASE OF HASHING POWER Hashing power providers agree to sell and NiceHash agrees to proceed Hashing power buyers’ payments for the provided hashing power on the Hashing power marketplace, on the Terms set forth herein. According to the applicable principle get-paid-per-valid-share (pay as you go principle) Hashing power providers will be paid only for validated and accepted hashing power to their NiceHash Wallet or other wallet, as indicated in Account holder’s profile settings or in stratum connection username. In some cases, no Hashing power is sent to Hashing power buyers or is accepted by NiceHash Services, even if Hashing power is generated on the Hashing power rigs. These cases include usage of slower hardware as well as software, hardware or network errors. In these cases, Hashing power providers are not paid for such Hashing power. Hashing power buyers agree to purchase and NiceHash agrees to process the order and forward the purchased hashing power on the Hashing power marketplace, on the Terms set forth herein. According to the applicable principle pay-per-valid-share (pay as you go principle) Hashing power buyers will pay from their NiceHash Wallet only for the hashing power that was validated by our engine. When connection to the mining pool which is selected on the Hashing power order is lost or when an order is cancelled during its lifetime, Hashing power buyer pays for additional 10 seconds worth of hashing power. Hashing power order is charged for extra hashing power when mining pool which is selected on the Hashing power order, generates rapid mining work changes and/or rapid mining job switching. All payments including any fees will be processed in crypto currency and NiceHash does not provide an option to sale and purchase of the hashing power in fiat currency. RISK DISCLOSURE If you choose to use NiceHash Platform, Services and NiceHash Wallet, it is important that you remain aware of the risks involved, that you have adequate technical resources and knowledge to bear such risks and that you monitor your transactions carefully. General risk You understand that NiceHash Platform and Services, blockchain technology, Bitcoin, all other cryptocurrencies and cryptotokens, proof of work concept and other associated and related technologies are new and untested and outside of NiceHash’s control. You acknowledge that there are major risks associated with these technologies. In addition to the risks disclosed below, there are risks that NiceHash cannot foresee and it is unreasonable to believe that such risk could have been foreseeable. The performance of NiceHash’s obligation under these Terms will terminate if market or technology circumstances change to such an extent that (i) these Terms clearly no longer comply with NiceHash’s expectations, (ii) it would be unjust to enforce NiceHash’s obligations in the general opinion or (iii) NiceHash’s obligation becomes impossible. NiceHash Account abuse You acknowledge that there is risk associated with the NiceHash Account abuse and that you have been fully informed and warned about it. The funds stored in the NiceHash Wallet may be disposed by third party in case the third party obtains the Account holder’s login credentials. The Account holder shall protect his login credentials and his electronic devices where the login credentials are stored against unauthorized access. Regulatory risks You acknowledge that there is risk associated with future legislation which may restrict, limit or prohibit certain aspects of blockchain technology which may also result in restriction, limitation or prohibition of NiceHash Services and that you have been fully informed and warned about it. Risk of hacking You acknowledge that there is risk associated with hacking NiceHash Services and NiceHash Wallet and that you have been fully informed and warned about it. Hacker or other groups or organizations may attempt to interfere with NiceHash Services or NiceHash Wallet in any way, including without limitation denial of services attacks, Sybil attacks, spoofing, smurfing, malware attacks, mining attacks or consensus-based attacks. Cryptocurrency risk You acknowledge that there is risk associated with the cryptocurrencies which are used as payment method and that you have been fully informed and warned about it. Cryptocurrencies are prone to, but not limited to, value volatility, transaction costs and times uncertainty, lack of liquidity, availability, regulatory restrictions, policy changes and security risks. NiceHash Wallet risk You acknowledge that there is risk associated with funds held on the NiceHash Wallet and that you have been fully informed and warned about it. You acknowledge that NiceHash Wallet is provided by NiceHash Wallet provider and not NiceHash. You acknowledge and agree that NiceHash shall not be responsible for any NiceHash Wallet provider’s services, including their accuracy, completeness, timeliness, validity, copyright compliance, legality, decency, quality or any other aspect thereof. NiceHash does not assume and shall not have any liability or responsibility to you or any other person or entity for any Hash Wallet provider’s services. Hash Wallet provider’s services and links thereto are provided solely as a convenience to you and you access and use them entirely at your own risk and subject to NiceHash Wallet provider’s terms and conditions. Since the NiceHash Wallet is a cryptocurrency wallet all funds held on it are entirely uninsured in contrast to the funds held on the bank account or other financial institutions which are insured. Connection risk You acknowledge that there are risks associated with usage of NiceHash Services which are provided through the internet including, but not limited to, the failure of hardware, software, configuration and internet connections and that you have been fully informed and warned about it. You acknowledge that NiceHash will not be responsible for any configuration, connection or communication failures, disruptions, errors, distortions or delays you may experience when using NiceHash Services, however caused. Hashing power provision risk You acknowledge that there are risks associated with the provisions of the hashing power which is provided by the Hashing power providers through the Hashing power marketplace and that you have been fully informed and warned about it. You acknowledge that NiceHash does not provide the hashing power but only provides the Hashing power marketplace as a service. Hashing power providers’ Hashing power rigs are new and untested and outside of NiceHash’s control. There is a major risk that the Hashing power rigs (i) will stop providing hashing power, (ii) will provide hashing power in an unstable way, (iii) will be wrongly configured or (iv) provide insufficient speed of the hashing power. Hashing power rigs as hardware could be subject of damage, errors, electricity outage, misconfiguration, connection or communication failures and other malfunctions. NiceHash will not be responsible for operation of Hashing power rigs and its provision of hashing power. By submitting a Hashing power order you agree to Hashing power no-refund policy – all shares forwarded to mining pool, selected on the Hashing power order are final and non-refundable. Hashing power profitability risk You acknowledge that there is risk associated with the profitability of the hashing power provision and that you have been fully informed and warned about it. You acknowledge that all Hashing power rig’s earning estimates and profitability calculations on NiceHash Platform are only for informational purposes and were made based on the Hashing power rigs set up in the test environments. NiceHash does not warrant that your Hashing power rigs would achieve the same profitability or earnings as calculated on NiceHash Platform. There is risk that your Hashing power rig would not produce desired hashing power quantity and quality and that your produced hashing power would differentiate from the hashing power produced by our Hashing power rigs set up in the test environments. There is risk that your Hashing power rigs would not be as profitable as our Hashing power rigs set up in the test environments or would not be profitable at all. WARRANTIES NiceHash Platform and Mining Services are provided on the “AS IS” and “AS AVAILABLE” basis, including all faults and defects. To the maximum extent permitted by applicable law, NiceHash makes no representations and warranties and you waive all warranties of any kind. Particularly, without limiting the generality of the foregoing, the NiceHash makes no representations and warranties, whether express, implied, statutory or otherwise regarding NiceHash Platform and Mining Services or other services related to NiceHash Platform and provided by third parties, including any warranty that such services will be uninterrupted, harmless, secure or not corrupt or damaged, meet your requirements, achieve any intended results, be compatible or work with any other software, applications, systems or services, meet any performance or error free or that any errors or defects can or will be corrected. Additionally NiceHash makes no representations and warranties, whether express, implied, statutory or otherwise of merchantability, suitability, reliability, availability, timeliness, accuracy, satisfactory quality, fitness for a particular purpose or quality, title and non-infringement with respect to any of the Mining Services or other services related to NiceHash Platform and provided by third parties, or quiet enjoyment and any warranties arising out of any course of dealing, course of performance, trade practice or usage of NiceHash Platform and Mining Services including information, content and material contained therein. Especially NiceHash makes no representations and warranties, whether express, implied, statutory or otherwise regarding any payment services and systems, NiceHash Wallet which is provided by third party or any other financial services which might be related to the NiceHash Platform and Mining Services. You acknowledge that you do not rely on and have not been induced to accept the NiceHash Platform and Mining Services according to these Terms on the basis of any warranties, representations, covenants, undertakings or any other statement whatsoever, other than expressly set out in these Terms that neither the NiceHash nor any of its respective agents, officers, employees or advisers have given any such warranties, representations, covenants, undertakings or other statements. LIABILITY NiceHash and their respective officers, employees or agents will not be liable to you or anyone else, to the maximum extent permitted by applicable law, for any damages of any kind, including, but not limited to, direct, consequential, incidental, special or indirect damages (including but not limited to lost profits, trading losses or damages that result from use or loss of use of NiceHash Services or NiceHash Wallet), even if NiceHash has been advised of the possibility of such damages or losses, including, without limitation, from the use or attempted use of NiceHash Platform and Mining Services, NiceHash Wallet or other related websites or services. NiceHash does not assume any obligations to users in connection with the unlawful alienation of Bitcoins, which occurred on 6. 12. 2017 with NICEHASH, d. o. o., and has been fully reimbursed with the completion of the NiceHash Repayment Program. NiceHash will not be responsible for any compensation, reimbursement, or damages arising in connection with: (i) your inability to use the NiceHash Platform and Mining Services, including without limitation as a result of any termination or suspension of the NiceHash Platform or these Terms, power outages, maintenance, defects, system failures, mistakes, omissions, errors, defects, viruses, delays in operation or transmission or any failure of performance, (ii) the cost of procurement of substitute goods or services, (iii) any your investments, expenditures, or commitments in connection with these Terms or your use of or access to the NiceHash Platform and Mining Services, (iv) your reliance on any information obtained from NiceHash, (v) Force Majeure Event, communications failure, theft or other interruptions or (vi) any unauthorized access, alteration, deletion, destruction, damage, loss or failure to store any data, including records, private key or other credentials, associated with NiceHash Platform and Mining Services or NiceHash Wallet. Our aggregate liability (including our directors, members, employees and agents), whether in contract, warranty, tort (including negligence, whether active, passive or imputed), product liability, strict liability or other theory, arising out of or relating to the use of NiceHash Platform and Mining Services, or inability to use the Platform and Services under these Terms or under any other document or agreement executed and delivered in connection herewith or contemplated hereby, shall in any event not exceed 100 EUR per user. You will defend, indemnify, and hold NiceHash harmless and all respective employees, officers, directors, and representatives from and against any claims, demand, action, damages, loss, liabilities, costs and expenses (including reasonable attorney fees) arising out of or relating to (i) any third-party claim concerning these Terms, (ii) your use of, or conduct in connection with, NiceHash Platform and Mining Services, (iii) any feedback you provide, (iv) your violation of these Terms, (v) or your violation of any rights of any other person or entity. If you are obligated to indemnify us, we will have the right, in our sole discretion, to control any action or proceeding (at our expense) and determine whether we wish to settle it. If we are obligated to respond to a third-party subpoena or other compulsory legal order or process described above, you will also reimburse us for reasonable attorney fees, as well as our employees’ and contractors’ time and materials spent responding to the third-party subpoena or other compulsory legal order or process at reasonable hourly rates. The Services and the information, products, and services included in or available through the NiceHash Platform may include inaccuracies or typographical errors. Changes are periodically added to the information herein. Improvements or changes on the NiceHash Platform can be made at any time. NICEHASH ACCOUNT The registration of the NiceHash Account is made through the NiceHash Platform, where you are required to enter your email address and password in the registration form. After successful completion of registration, the confirmation email is sent to you. After you confirm your registration by clicking on the link in the confirmation email the NiceHash Account is created. NiceHash will send you proof of completed registration once the process is completed. When you create NiceHash Account, you agree to (i) create a strong password that you change frequently and do not use for any other website, (ii) implement reasonable and appropriate measures designed to secure access to any device which has access to your email address associated with your NiceHash Account and your username and password for your NiceHash Account, (iii) maintain the security of your NiceHash Account by protecting your password and by restricting access to your NiceHash Account; (iv) promptly notify us if you discover or otherwise suspect any security breaches related to your NiceHash Account so we can take all required and possible measures to secure your NiceHash Account and (v) take responsibility for all activities that occur under your NiceHash Account and accept all risks of any authorized or unauthorized access to your NiceHash Account, to the maximum extent permitted by law. Losing access to your email, registered at NiceHash Platform, may also mean losing access to your NiceHash Account. You may not be able to use the NiceHash Platform or Mining Services, execute withdrawals and other security sensitive operations until you regain access to your email address, registered at NiceHash Platform. If you wish to change the email address linked to your NiceHash Account, we may ask you to complete a KYC procedure for security purposes. This step serves solely for the purpose of identification in the process of regaining access to your NiceHash Account. Once the NiceHash Account is created a NiceHash Wallet is automatically created for the NiceHash Account when the request for the first deposit to the NiceHash Wallet is made by the user. Account holder’s NiceHash Wallet is generated by NiceHash Wallet provider. Account holder is strongly suggested to enhance the security of his NiceHash Account by adding an additional security step of Two-factor authentication (hereinafter “2FA”) when logging into his account, withdrawing funds from his NiceHash Wallet or placing a new order. Account holder can enable this security feature in the settings of his NiceHash Account. In the event of losing or changing 2FA code, we may ask the Account holder to complete a KYC procedure for security reasons. This step serves solely for the purpose of identification in the process of reactivating Account holders 2FA and it may be subject to an a In order to use certain functionalities of the NiceHash Platform, such as paying for the acquired hashing power, users must deposit funds to the NiceHash Wallet, as the payments for the hashing power could be made only through NiceHash Wallet. Hashing power providers have two options to get paid for the provided hashing power: (i) by using NiceHash Wallet to receive the payments or (ii) by providing other Bitcoin address where the payments shall be received to. Hashing power providers provide their Bitcoin address to NiceHash by providing such details via Account holder’s profile settings or in a form of a stratum username while connecting to NiceHash stratum servers. Account holder may load funds on his NiceHash Wallet from his Secondary account. Account holder may be charged fees by the Secondary account provider or by the blockchain network for such transaction. NiceHash is not responsible for any fees charged by Secondary account providers or by the blockchain network or for the management and security of the Secondary accounts. Account holder is solely responsible for his use of Secondary accounts and Account holder agrees to comply with all terms and conditions applicable to any Secondary accounts. The timing associated with a load transaction will depend in part upon the performance of Secondary accounts providers, the performance of blockchain network and performance of the NiceHash Wallet provider. NiceHash makes no guarantee regarding the amount of time it may take to load funds on to NiceHash Wallet. NiceHash Wallet shall not be used by Account holders to keep, save and hold funds for longer period and also not for executing other transactions which are not related to the transactions regarding the NiceHash Platform. The NiceHash Wallet shall be used exclusively and only for current and ongoing transactions regarding the NiceHash Platform. Account holders shall promptly withdraw any funds kept on the NiceHash Wallet that will not be used and are not intended for the reasons described earlier. Commission fees may be charged by the NiceHash Wallet provider, by the blockchain network or by NiceHash for any NiceHash Wallet transactions. Please refer to the NiceHash Platform, for more information about the commission fees for NiceHash Wallet transactions which are applicable at the time of the transaction. NiceHash reserves the right to change these commission fees according to the provisions to change these Terms at any time for any reason. You have the right to use the NiceHash Account only in compliance with these Terms and other commercial terms and principles published on the NiceHash Platform. In particular, you must observe all regulations aimed at ensuring the security of funds and financial transactions. Provided that the balance of funds in your NiceHash Wallet is greater than any minimum balance requirements needed to satisfy any of your open orders, you may withdraw from your NiceHash Wallet any amount of funds, up to the total amount of funds in your NiceHash Wallet in excess of such minimum balance requirements, to Secondary Account, less any applicable withdrawal fees charged by NiceHash or by the blockchain network for such transaction. Withdrawals are not processed instantly and may be grouped with other withdrawal requests. Some withdrawals may require additional verification information which you will have to provide in order to process the withdrawal. It may take up to 24 hours before withdrawal is fully processed and distributed to the Blockchain network. Please refer to the NiceHash Platform for more information about the withdrawal fees and withdrawal processing. NiceHash reserves the right to change these fees according to the provisions to change these Terms at any time for any reason. You have the right to close the NiceHash Account. In case you have funds on your NiceHash Wallet you should withdraw funds from your account prior to requesting NiceHash Account closure. After we receive your NiceHash Account closure request we will deactivate your NiceHash Account. You can read more about closing the NiceHash Account in our Privacy Policy. Your NiceHash Account may be deactivated due to your inactivity. Your NiceHash account may be locked and a mandatory KYC procedure is applied for security reasons, if it has been more than 6 month since your last login. NiceHash or any of its partners or affiliates are not responsible for the loss of the funds, stored on or transferred from the NiceHash Wallet, as well as for the erroneous implementation of the transactions made via NiceHash Wallet, where such loss or faulty implementation of the transaction are the result of a malfunction of the NiceHash Wallet and the malfunction was caused by you or the NiceHash Wallet provider. You are obliged to inform NiceHash in case of loss or theft, as well as in the case of any possible misuse of the access data to your NiceHash Account, without any delay, and demand change of access data or closure of your existing NiceHash Account and submit a request for new access data. NiceHash will execute the change of access data or closure of the NiceHash Account and the opening of new NiceHash Account as soon as technically possible and without any undue delay. All information pertaining to registration, including a registration form, generation of NiceHash Wallet and detailed instructions on the use of the NiceHash Account and NiceHash Wallet are available at NiceHash Platform. The registration form as well as the entire system is properly protected from unwanted interference by third parties. KYC PROCEDURE NiceHash is appropriately implementing AML/CTF and security measures to diligently detect and prevent any malicious or unlawful use of NiceHash Services or use, which is strictly prohibited by these Terms, which are deemed as your agreement to provide required personal information for identity verification. Security measures include a KYC procedure, which is aimed at determining the identity of an individual user or an organisation. We may ask you to complete this procedure before enabling some or all functionalities of the NiceHash platform and provide its services. A KYC procedure might be applied as a security measure when: changing the email address linked to your NiceHash Account, 
 losing or changing your 2FA code; 
 logging in to your NiceHash Account for the first time after the launch of the new NiceHash Platform in August 2019, 
 gaining access to all or a portion of NiceHash Services, NiceHash Wallet and its related services or any portion thereof if they were disabled due to and 
 activating your NiceHash Account if it has been deactivated due to its inactivity and/or security or other reasons. 
 HASHING POWER TRANSACTIONS General NiceHash may, at any time and in our sole discretion, (i) refuse any order submitted or provided hashing power, (ii) cancel an order or part of the order before it is executed, (iii) impose limits on the order amount permitted or on provided hashing power or (iv) impose any other conditions or restrictions upon your use of the NiceHash Platform and Mining Services without prior notice. For example, but not limited to, NiceHash may limit the number of open orders that you may establish or limit the type of supported Hashing power rigs and mining algorithms or NiceHash may restrict submitting orders or providing hashing power from certain locations. Please refer to the NiceHash Platform, for more information about terminology, hashing power transactions’ definitions and descriptions, order types, order submission, order procedure, order rules and other restrictions and limitations of the hashing power transactions. NiceHash reserves the right to change any transaction, definitions, description, order types, procedure, rules, restrictions and limitations at any time for any reason. Orders, provision of hashing power, payments, deposits, withdrawals and other transactions are accepted only through the interface of the NiceHash Platform, NiceHash API and NiceHash Account and are fixed by the software and hardware tools of the NiceHash Platform. If you do not understand the meaning of any transaction option, NiceHash strongly encourages you not to utilize any of those options. Hashing Power Order In order to submit an Hashing Power Order via the NiceHash Account, the Hashing power buyer must have available funds in his NiceHash Wallet. Hashing power buyer submits a new order to buy hashing power via the NiceHash Platform or via the NiceHash API by setting the following parameters in the order form: NiceHash service server location, third-party mining pool, algorithm to use, order type, set amount he is willing to spend on this order, set price per hash he is willing to pay, optionally approximate limit maximum hashing power for his order and other parameters as requested and by confirming his order. Hashing power buyer may submit an order in maximum amount of funds available on his NiceHash Wallet at the time of order submission. Order run time is only approximate since order’s lifetime is based on the number of hashes that it delivers. Particularly during periods of high volume, illiquidity, fast movement or volatility in the marketplace for any digital assets or hashing power, the actual price per hash at which some of the orders are executed may be different from the prevailing price indicated on NiceHash Platform at the time of your order. You understand that NiceHash is not liable for any such price fluctuations. In the event of market disruption, NiceHash Services disruption, NiceHash Hashing Power Marketplace disruption or manipulation or Force Majeure Event, NiceHash may do one or more of the following: (i) suspend access to the NiceHash Account or NiceHash Platform, or (ii) prevent you from completing any actions in the NiceHash Account, including closing any open orders. Following any such event, when trading resumes, you acknowledge that prevailing market prices may differ significantly from the prices available prior to such event. When Hashing power buyer submits an order for purchasing of the Hashing power via NiceHash Platform or via the NiceHash API he authorizes NiceHash to execute the order on his behalf and for his account in accordance with such order. Hashing power buyer acknowledges and agrees that NiceHash is not acting as his broker, intermediary, agent or advisor or in any fiduciary capacity. NiceHash executes the order in set order amount minus NiceHash’s processing fee. Once the order is successfully submitted the order amount starts to decrease in real time according to the payments for the provided hashing power. Hashing power buyer agrees to pay applicable processing fee to NiceHash for provided services. The NiceHash’s fees are deducted from Hashing power buyer’s NiceHash Wallet once the whole order is exhausted and completed. Please refer to the NiceHash Platform, for more information about the fees which are applicable at the time of provision of services. NiceHash reserves the right to change these fees according to the provisions to change these Terms at any time for any reason. The changed fees will apply only for the NiceHash Services provided after the change of the fees. All orders submitted prior the fee change but not necessary completed prior the fee change will be charged according to the fees applicable at the time of the submission of the order. NiceHash will attempt, on a commercially reasonable basis, to execute the Hashing power buyer’s purchase of the hashing power on the Hashing power marketplace under these Terms according to the best-effort delivery approach. In this respect NiceHash does not guarantee that the hashing power will actually be delivered or verified and does not guarantee any quality of the NiceHash Services. Hashing power buyer may cancel a submitted order during order’s lifetime. If an order has been partially executed, Hashing power buyer may cancel the unexecuted remainder of the order. In this case the NiceHash’s processing fee will apply only for the partially executed order. NiceHash reserves the right to refuse any order cancellation request once the order has been submitted. Selling Hashing Power and the Provision of Hashing Power In order to submit the hashing power to the NiceHash stratum server the Hashing power provider must first point its Hashing power rig to the NiceHash stratum server. Hashing power provider is solely responsible for configuration of his Hashing power rig. The Hashing power provider gets paid by Hashing power buyers for all validated and accepted work that his Hashing power rig has produced. The provided hashing power is validated by NiceHash’s stratum engine and validator. Once the hashing power is validated the Hashing power provider is entitled to receive the payment for his work. NiceHash logs all validated hashing power which was submitted by the Hashing power provider. The Hashing power provider receives the payments of current globally weighted average price on to his NiceHash Wallet or his selected personal Bitcoin address. The payments are made periodically depending on the height of payments. NiceHash reserves the right to hold the payments any time and for any reason by indicating the reason, especially if the payments represent smaller values. Please refer to the NiceHash Platform, for more information about the height of payments for provided hashing power, how the current globally weighted average price is calculated, payment periods, payment conditions and conditions for detention of payments. NiceHash reserves the right to change this payment policy according to the provisions to change these Terms at any time for any reason. All Hashing power rig’s earnings and profitability calculations on NiceHash Platform are only for informational purposes. NiceHash does not warrant that your Hashing power rigs would achieve the same profitability or earnings as calculated on NiceHash Platform. You hereby acknowledge that it is possible that your Hashing power rigs would not be as profitable as indicated in our informational calculations or would not be profitable at all. Hashing power provider agrees to pay applicable processing fee to NiceHash for provided Services. The NiceHash’s fees are deducted from all the payments made to the Hashing power provider for his provided work. Please refer to the NiceHash Platform, for more information about the fees which are applicable at the time of provision of services. Hashing power provider which has not submitted any hashing power to the NiceHash stratum server for a period of 90 days agrees that a processing fee of 0.00001000 BTC or less, depending on the unpaid mining balance, will be deducted from his unpaid mining balance. NiceHash reserves the right to change these fees according to the provisions to change these Terms at any time for any reason. The changed fees will apply only for the NiceHash Services provided after the change of the fees. NiceHash will attempt, on a commercially reasonable basis, to execute the provision of Hashing power providers’ hashing power on the Hashing power marketplace under these Terms according to the best-effort delivery approach. In this respect NiceHash does not guarantee that the hashing power will actually be delivered or verified and does not guarantee any quality of the NiceHash Services. Hashing power provider may disconnect the Hashing power rig from the NiceHash stratum server any time. NiceHash reserves the right to refuse any Hashing power rig once the Hashing power rig has been pointed towards NiceHash stratum server. RESTRICTIONS When accessing the NiceHash Platform or using the Mining Services or NiceHash Wallet, you warrant and agree that you: will not use the Services for any purpose that is unlawful or prohibited by these Terms,
 will not violate any law, contract, intellectual property or other third-party right or commit a tort,
 are solely responsible for your conduct while accessing the NiceHash Platform or using the Mining Services or NiceHash Wallet,
 will not access the NiceHash Platform or use the Mining Services in any manner that could damage, disable, overburden, or impair the provision of the Services or interfere with any other party's use and enjoyment of the Services, 
 will not misuse and/or maliciously use Hashing power rigs, you will particularly refrain from using network botnets or using NiceHash Platform or Mining Services with Hashing power rigs without the knowledge or awareness of Hashing power rig owner(s), 
 will not perform or attempt to perform any kind of malicious attacks on blockchains with the use of the NiceHash Platform or Mining Services, intended to maliciously gain control of more than 50% of the network's mining hash rate, 
 will not use the NiceHash Platform or Mining Services for any kind of market manipulation or disruption, such as but not limited to NiceHash Mining Services disruption and NiceHash Hashing Power Marketplace manipulation.
 In case of any of the above mentioned events, NiceHash reserves the right to immediately suspend your NiceHash Account, freeze or block the funds in the NiceHash Wallet, and suspend your access to NiceHash Platform, particularly if NiceHash believes that such NiceHash Account are in violation of these Terms or Privacy Policy, or any applicable laws and regulation. RIGHTS AND OBLIGATIONS In the event of disputes with you, NiceHash is obliged to prove that the NiceHash service which is the subject of the dispute was not influenced by technical or other failure. You will have possibility to check at any time, subject to technical availability, the transactions details, statistics and available balance of the funds held on the NiceHash Wallet, through access to the NiceHash Account. You may not obtain or attempt to obtain any materials or information through any means not intentionally made available or provided to you or public through the NiceHash Platform or Mining Services. We may, in our sole discretion, at any time, for any or no reason and without liability to you, with prior notice (i) terminate all rights and obligations between you and NiceHash derived from these Terms, (ii) suspend your access to all or a portion of NiceHash Services, NiceHash Wallet and its related services or any portion thereof and delete or deactivate your NiceHash Account and all related information and files in such account (iii) modify, suspend or discontinue, temporarily or permanently, any portion of NiceHash Platform or (iv) provide enhancements or improvements to the features and functionality of the NiceHash Platform, which may include patches, bug fixes, updates, upgrades and other modifications. Any such change may modify or delete certain portion, features or functionalities of the NiceHash Services. You agree that NiceHash has no obligation to (i) provide any updates, or (ii) continue to provide or enable any particular portion, features or functionalities of the NiceHash Services to you. You further agree that all changes will be (i) deemed to constitute an integral part of the NiceHash Platform, and (ii) subject to these Terms. In the event of your breach of these Terms, including but not limited to, for instance, in the event that you breach any term of these Terms, due to legal grounds originating in anti-money laundering and know your client regulation and procedures, or any other relevant applicable regulation, all right and obligations between you and NiceHash derived from these Terms terminate automatically if you fail to comply with these Terms within the notice period of 8 days after you have been warned by NiceHash about the breach and given 8 days period to cure the breaches. NiceHash reserves the right to keep these rights and obligations in force despite your breach of these Terms. In the event of termination, NiceHash will attempt to return you any funds stored on your NiceHash Wallet not otherwise owed to NiceHash, unless NiceHash believes you have committed fraud, negligence or other misconduct. You acknowledge that the NiceHash Services and NiceHash Wallet may be suspended for maintenance. Technical information about the hashing power transactions, including information about chosen server locations, algorithms used, selected mining pools, your business or activities, including all financial and technical information, specifications, technology together with all details of prices, current transaction performance and future business strategy represent confidential information and trade secrets. NiceHash shall, preserve the confidentiality of all before mentioned information and shall not disclose or cause or permit to be disclosed without your permission any of these information to any person save to the extent that such disclosure is strictly to enable you to perform or comply with any of your obligations under these Terms, or to the extent that there is an irresistible legal requirement on you or NiceHash to do so; or where the information has come into the public domain otherwise than through a breach of any of the terms of these Terms. NiceHash shall not be entitled to make use of any of these confidential information and trade secrets other than during the continuance of and pursuant to these Terms and then only for the purpose of carrying out its obligations pursuant to these Terms. NICEHASH MINER LICENSE (NICEHASH MINING SOFTWARE LICENSE) NiceHash Mining Software whether on disk, in read only memory, or any other media or in any other form is licensed, not sold, to you by NiceHash for use only under these Terms. NiceHash retains ownership of the NiceHash Mining Software itself and reserves all rights not expressly granted to you. Subject to these Terms, you are granted a limited, non-transferable, non-exclusive and a revocable license to download, install and use the NiceHash Mining Software. You may not distribute or make the NiceHash Mining Software available over a network where it could be used by multiple devices at the same time. You may not rent, lease, lend, sell, redistribute, assign, sublicense host, outsource, disclose or otherwise commercially exploit the NiceHash Mining Software or make it available to any third party. There is no license fee for the NiceHash Mining Software. NiceHash reserves the right to change the license fee policy according to the provisions to change these Terms any time and for any reason, including to decide to start charging the license fee for the NiceHash Mining Software. You are responsible for any and all applicable taxes. You may not, and you agree not to or enable others to, copy, decompile, reverse engineer, reverse compile, disassemble, attempt to derive the source code of, decrypt, modify, or create derivative works of the NiceHash Mining Software or any services provided by the NiceHash Mining Software, or any part thereof (except as and only to the extent any foregoing restriction is prohibited by applicable law or to the extent as may be permitted by the licensing terms governing use of open-sourced components included with the NiceHash Mining Software). If you choose to allow automatic updates, your device will periodically check with NiceHash for updates and upgrades to the NiceHash Mining Software and, if an update or upgrade is available, the update or upgrade will automatically download and install onto your device and, if applicable, your peripheral devices. You can turn off the automatic updates altogether at any time by changing the automatic updates settings found within the NiceHash Mining Software. You agree that NiceHash may collect and use technical and related information, including but not limited to technical information about your computer, system and application software, and peripherals, that is gathered periodically to facilitate the provision of software updates, product support and other services to you (if any) related to the NiceHash Mining Software and to verify compliance with these Terms. NiceHash may use this information, as long as it is in a form that does not personally identify you, to improve our NiceHash Services. NiceHash Mining Software contains features that rely upon information about your selected mining pools. You agree to our transmission, collection, maintenance, processing, and use of all information obtained from you about your selected mining pools. You can opt out at any time by going to settings in the NiceHash Mining Software. NiceHash may provide interest-based advertising to you. If you do not want to receive relevant ads in the NiceHash Mining Software, you can opt out at any time by going to settings in the NiceHash Mining Software. If you opt out, you will continue to receive the same number of ads, but they may be less relevant because they will not be based on your interest. NiceHash Mining Software license is effective until terminated. All provisions of these Terms regarding the termination apply also for the NiceHash Mining Software license. Upon the termination of NiceHash Mining Software license, you shall cease all use of the NiceHash Mining Software and destroy or delete all copies, full or partial, of the NiceHash Mining Software. THIRD PARTY MINERS AND PLUGINS Third Party Miners and Plugins are a third party software which enables the best and most efficient mining operations. NiceHash Miner integrates third party mining software using a third party miner plugin system. Third Party Mining Software is a closed source software which supports mining algorithms for cryptocurrencies and can be integrated into NiceHash Mining Software. Third Party Miner Plugin enables the connection between NiceHash Mining Software and Third Party Mining Software and it can be closed, as well as open sourced. NiceHash Mining Software user interface enables the user to manually select which available Third Party Miners and Plugins will be downloaded and integrated. Users can select or deselect Third Party Miners and Plugins found in the Plugin Manager window. Some of the available Third Party Miners and Plugins which are most common are preselected by NiceHash, but can be deselected, depending on users' needs. The details of the Third Party Miners and Plugins available for NiceHash Mining Software are accessible within the NiceHash Mining Software user interface. The details include, but not limited to, the author of the software and applicable license information, if applicable information about developer fee for Third Party Miners, software version etc. Developer fees may apply to the use of Third Party Miners and Plugins. NiceHash will not be liable, to the maximum extent permitted by applicable law, for any damages of any kind, including, but not limited to, direct, consequential, incidental, special or indirect damages, arising out of using Third Party Miners and Plugins. The latter includes, but is not limited to: i) any power outages, maintenance, defects, system failures, mistakes, omissions, errors, defects, viruses, delays in operation or transmission or any failure of performance; ii) any unauthorized access, alteration, deletion, destruction, damage, loss or failure to store any data, including records, private key or other credentials, associated with usage of Third Party Miners and Plugins and ii) Force Majeure Event, communications failure, theft or other interruptions. If you choose to allow automatic updates, your device will periodically check with NiceHash for updates and upgrades to the installed Third Party Miners and Plugins, if an update or upgrade is available, the update or upgrade will automatically download and install onto your device and, if applicable, your peripheral devices. You can turn off the automatic updates altogether at any time by changing the automatic updates settings found within the NiceHash Mining Software. NICEHASH QUICKMINER NiceHash QuickMiner is a software application that allows the visitors of the NiceHash Quick Miner web page, accessible athttps://www.nicehash.com/quick-miner, to connect their PC or a mining rig to the NiceHash Hashing Power Marketplace. Visitors of the NiceHash Quick Miner web page can try out and experience crypto currency mining without having to register on the NiceHash Platform and create a NiceHash Account. Users are encouraged to do so as soon as possible in order to collect the funds earned using NiceHash Quick Miner. Users can download NiceHash QuickMiner free of charge. In order to operate NiceHash QuickMiner software needs to automatically detect technical information about users' computer hardware. You agree that NiceHash may collect and use technical and related information. For more information please refer to NiceHash Privacy Policy. Funds arising from the usage of NiceHash QuickMiner are transferred to a dedicated cryptocurrency wallet owned and managed by NiceHash. NiceHash QuickMiner Users expressly agree and acknowledge that completing the registration process and creating a NiceHash Account is necessary in order to collect the funds arising from the usage of NiceHash QuickMiner. Users of NiceHash QuickMiner who do not successfully register a NiceHash Account will lose their right to claim funds arising from their usage of NiceHash QuickMiner. Those funds, in addition to the condition that the user has not been active on the NiceHash QuickMiner web page for consecutive 7 days, will be donated to the charity of choice. NICEHASH PRIVATE ENDPOINT NiceHash Private Endpoint is a network interface that connects users privately and securely to NiceHash Stratum servers. Private Endpoint uses a private IP address and avoids additional latency caused by DDOS protection. All NiceHash Private Mining Proxy servers are managed by NiceHash and kept up-to-date. Users can request a dedicated private access endpoint by filling in the form for NiceHash Private Endpoint Solution available at the NiceHash Platform. In the form the user specifies the email address, country, number of connections and locations and algorithms used. Based on the request NiceHash prepares an individualized offer based on the pricing stipulated on the NiceHash Platform, available at https://www.nicehash.com/private-endpoint-solution. NiceHash may request additional information from the users of the Private Endpoint Solution in order to determine whether we are obligated to collect VAT from you, including your VAT identification number. INTELLECTUAL PROPERTY NiceHash retains all copyright and other intellectual property rights, including inventions, discoveries, knowhow, processes, marks, methods, compositions, formulae, techniques, information and data, whether or not patentable, copyrightable or protectable in trademark, and any trademarks, copyrights or patents based thereon over all content and other materials contained on NiceHash Platform or provided in connection with the Services, including, without limitation, the NiceHash logo and all designs, text, graphics, pictures, information, data, software, source code, as well as the compilation thereof, sound files, other files and the selection and arrangement thereof. This material is protected by international copyright laws and other intellectual property right laws, namely trademark. These Terms shall not be understood and interpreted in a way that they would mean assignment of copyright or other intellectual property rights, unless it is explicitly defined so in these Terms. NiceHash hereby grants you a limited, nonexclusive and non-sublicensable license to access and use NiceHash’s copyrighted work and other intellectual property for your personal or internal business use. Such license is subject to these Terms and does not permit any resale, the distribution, public performance or public display, modifying or otherwise making any derivative uses, use, publishing, transmission, reverse engineering, participation in the transfer or sale, or any way exploit any of the copyrighted work and other intellectual property other than for their intended purposes. This granted license will automatically terminate if NiceHash suspends or terminates your access to the Services, NiceHash Wallet or closes your NiceHash Account. NiceHash will own exclusive rights, including all intellectual property rights, to any feedback including, but not limited to, suggestions, ideas or other information or materials regarding NiceHash Services or related products that you provide, whether by email, posting through our NiceHash Platform, NiceHash Account or otherwise and you irrevocably assign any and all intellectual property rights on such feedback unlimited in time, scope and territory. Any Feedback you submit is non-confidential and shall become the sole property of NiceHash. NiceHash will be entitled to the unrestricted use, modification or dissemination of such feedback for any purpose, commercial or otherwise, without acknowledgment or compensation to you. You waive any rights you may have to the feedback. We have the right to remove any posting you make on NiceHash Platform if, in our opinion, your post does not comply with the content standards defined by these Terms. PRIVACY POLICY Please refer to our NiceHash Platform and Mining Services Privacy Policy published on the NiceHash Platform for information about how we collect, use and share your information, as well as what options do you have with regards to your personal information. COMMUNICATION AND SUPPORT You agree and consent to receive electronically all communications, agreements, documents, receipts, notices and disclosures that NiceHash provides in connection with your NiceHash Account or use of the NiceHash Platform and Services. You agree that NiceHash may provide these communications to you by posting them via the NiceHash Account or by emailing them to you at the email address you provide. You should maintain copies of electronic communications by printing a paper copy or saving an electronic copy. It is your responsibility to keep your email address updated in the NiceHash Account so that NiceHash can communicate with you electronically. You understand and agree that if NiceHash sends you an electronic communication but you do not receive it because your email address is incorrect, out of date, blocked by your service provider, or you are otherwise unable to receive electronic communications, it will be deemed that you have been provided with the communication. You can update your NiceHash Account preferences at any time by logging into your NiceHash Account. If your email address becomes invalid such that electronic communications sent to you by NiceHash are returned, NiceHash may deem your account to be inactive and close it. You may give NiceHash a notice under these Terms by sending an email to support@nicehash.com or contact NiceHash through support located on the NiceHash Platform. All communication and notices pursuant to these Terms must be given in English language. FEES Please refer to the NiceHash Platform for more information about the fees or administrative costs which are applicable at the time of provision of services. NiceHash reserves the right to change these fees according to the provisions to change these Terms at any time for any reason. The changed fees will apply only for the Services provided after the change of the fees. You authorize us, or our designated payment processor, to charge or deduct your NiceHash Account for any applicable fees in connection with the transactions completed via the Services. TAX It is your responsibility to determine what, if any, taxes apply to the transactions you complete or services you provide via the NiceHash Platform, Mining Services and NiceHash Wallet, it is your responsibility to report and remit the correct tax to the appropriate tax authority and all your factual and potential tax obligations are your concern. You agree that NiceHash is not in any case and under no conditions responsible for determining whether taxes apply to your transactions or services or for collecting, reporting, withholding or remitting any taxes arising from any transactions or services. You also agree that NiceHash is not in any case and under no conditions bound to compensate for your tax obligation or give you any advice related to tax issues. All fees and charges payable by you to NiceHash are exclusive of any taxes, and shall certain taxes be applicable, they shall be added on top of the payable amounts. Upon our request, you will provide to us any information that we reasonably request to determine whether we are obligated to collect VAT from you, including your VAT identification number. If any deduction or withholding is required by law, you will notify NiceHash and will pay NiceHash any additional amounts necessary to ensure that the net amount received by NiceHash, after any deduction and withholding, equals the amount NiceHash would have received if no deduction or withholding had been required. Additionally, you will provide NiceHash with documentation showing that the withheld and deducted amounts have been paid to the relevant taxing authority. FINAL PROVISIONS Natural persons and legal entities that are not capable of holding legal rights and obligations are not allowed to create NiceHash Account and use NiceHash Platform or other related services. If NiceHash becomes aware that such natural person or legal entity has created the NiceHash Account or has used NiceHash Services, NiceHash will delete such NiceHash Account and disable any Services and block access to NiceHash Account and NiceHash Services to such natural person or legal entity. If you register to use the NiceHash Services on behalf of a legal entity, you represent and warrant that (i) such legal entity is duly organized and validly existing under the applicable laws of the jurisdiction of its organization; and (ii) you are duly authorized by such legal entity to act on its behalf. These Terms do not create any third-party beneficiary rights in any individual or entity. These Terms forms the entire agreement and understanding relating to the subject matter hereof and supersede any previous and contemporaneous agreements, arrangements or understandings relating to the subject matter hereof to the exclusion of any terms implied by law that may be excluded by contract. If at any time any provision of these Terms is or becomes illegal, invalid or unenforceable, the legality, validity and enforceability of every other provisions will not in any way be impaired. Such illegal, invalid or unenforceable provision of these Terms shall be deemed to be modified and replaced by such legal, valid and enforceable provision or arrangement, which corresponds as closely as possible to our and your will and business purpose pursued and reflected in these Terms. Headings of sections are for convenience only and shall not be used to limit or construe such sections. No failure to enforce nor delay in enforcing, on our side to the Terms, any right or legal remedy shall function as a waiver thereof, nor shall any individual or partial exercise of any right or legal remedy prevent any further or other enforcement of these rights or legal remedies or the enforcement of any other rights or legal remedies. NiceHash reserves the right to make changes, amendments, supplementations or modifications from time to time to these Terms including but not limited to changes of licence agreement for NiceHash Mining Software and of any fees and compensations policies, in its sole discretion and for any reason. We suggest that you review these Terms periodically for changes. If we make changes to these Terms, we will provide you with notice of such changes, such as by sending an email, providing notice on the NiceHash Platform, placing a popup window after login to the NiceHash Account or by posting the amended Terms on the NiceHash Platform and updating the date at the top of these Terms. The amended Terms will be deemed effective immediately upon posting for any new users of the NiceHash Services. In all other cases, the amended Terms will become effective for preexisting users upon the earlier of either: (i) the date users click or press a button to accept such changes in their NiceHash Account, or (ii) continued use of NiceHash Services 30 days after NiceHash provides notice of such changes. Any amended Terms will apply prospectively to use of the NiceHash Services after such changes become effective. The notice of change of these Terms is considered as notice of termination of all rights and obligations between you and NiceHash derived from these Terms with notice period of 30 days, if you do not accept the amended Terms. If you do not agree to any amended Terms, (i) the agreement between you and NiceHash is terminated by expiry of 30 days period which starts after NiceHash provides you a notice of change of these Terms, (ii) you must discontinue using NiceHash Services and (iii) you must inform us regarding your disagreement with the changes and request closure of your NiceHash Account. If you do not inform us regarding your disagreement and do not request closure of you NiceHash Account, we will deem that you agree with the changed Terms. You may not assign or transfer your rights or obligations under these Terms without the prior written consent of NiceHash. NiceHash may assign or transfer any or all of its rights under these Terms, in whole or in part, without obtaining your consent or approval. These Terms shall be governed by and construed and enforced in accordance with the Laws of the British Virgin Islands, and shall be interpreted in all respects as a British Virgin Islands contract. Any transaction, dispute, controversy, claim or action arising from or related to your access or use of the NiceHash Platform or these Terms of Service likewise shall be governed by the Laws of the British Virgin Islands, exclusive of choice-of-law principles. The rights and remedies conferred on NiceHash by, or pursuant to, these Terms are cumulative and are in addition, and without prejudice, to all other rights and remedies otherwise available to NiceHash at law. NiceHash may transfer its rights and obligations under these Terms to other entities which include, but are not limited to H-BIT, d.o.o. and NICEX Ltd, or any other firm or business entity that directly or indirectly acquires all or substantially all of the assets or business of NICEHASH Ltd. If you do not consent to any transfer, you may terminate this agreement and close your NiceHash Account. These Terms are not boilerplate. If you disagree with any of them, believe that any should not apply to you, or wish to negotiate these Terms, please contact NiceHash and immediately navigate away from the NiceHash Platform. Do not use the NiceHash Mining Services, NiceHash Wallet or other related services until you and NiceHash have agreed upon new terms of service. Last updated: March 1, 2021

This is Complete Course on Applied Statistics for Data Scientists with R.

Materials for UC Berkeley Neuroscience 299

Note-of-Applied-Statistics-with-R

Case studies in applied and computational statistics.

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Archived (PDF) from the original on 4 September 2013. Retrieved 4 June 2013 – via msu.edu. "Applications of AI". www-formal.stanford.edu. Archived from the original on 28 August 2016. Retrieved 25 September 2016. Further reading DH Author, 'Why Are There Still So Many Jobs? The History and Future of Workplace Automation' (2015) 29(3) Journal of Economic Perspectives 3. Boden, Margaret, Mind As Machine, Oxford University Press, 2006. Cukier, Kenneth, "Ready for Robots? How to Think about the Future of AI", Foreign Affairs, vol. 98, no. 4 (July/August 2019), pp. 192–98. George Dyson, historian of computing, writes (in what might be called "Dyson's Law") that "Any system simple enough to be understandable will not be complicated enough to behave intelligently, while any system complicated enough to behave intelligently will be too complicated to understand." (p. 197.) Computer scientist Alex Pentland writes: "Current AI machine-learning algorithms are, at their core, dead simple stupid. They work, but they work by brute force." (p. 198.) Domingos, Pedro, "Our Digital Doubles: AI will serve our species, not control it", Scientific American, vol. 319, no. 3 (September 2018), pp. 88–93. Gopnik, Alison, "Making AI More Human: Artificial intelligence has staged a revival by starting to incorporate what we know about how children learn", Scientific American, vol. 316, no. 6 (June 2017), pp. 60–65. Johnston, John (2008) The Allure of Machinic Life: Cybernetics, Artificial Life, and the New AI, MIT Press. Koch, Christof, "Proust among the Machines", Scientific American, vol. 321, no. 6 (December 2019), pp. 46–49. Christof Koch doubts the possibility of "intelligent" machines attaining consciousness, because "[e]ven the most sophisticated brain simulations are unlikely to produce conscious feelings." (p. 48.) According to Koch, "Whether machines can become sentient [is important] for ethical reasons. If computers experience life through their own senses, they cease to be purely a means to an end determined by their usefulness to... humans. Per GNW [the Global Neuronal Workspace theory], they turn from mere objects into subjects... with a point of view.... Once computers' cognitive abilities rival those of humanity, their impulse to push for legal and political rights will become irresistible – the right not to be deleted, not to have their memories wiped clean, not to suffer pain and degradation. The alternative, embodied by IIT [Integrated Information Theory], is that computers will remain only supersophisticated machinery, ghostlike empty shells, devoid of what we value most: the feeling of life itself." (p. 49.) Marcus, Gary, "Am I Human?: Researchers need new ways to distinguish artificial intelligence from the natural kind", Scientific American, vol. 316, no. 3 (March 2017), pp. 58–63. A stumbling block to AI has been an incapacity for reliable disambiguation. An example is the "pronoun disambiguation problem": a machine has no way of determining to whom or what a pronoun in a sentence refers. (p. 61.) E McGaughey, 'Will Robots Automate Your Job Away? Full Employment, Basic Income, and Economic Democracy' (2018) SSRN, part 2(3) Archived 24 May 2018 at the Wayback Machine. George Musser, "Artificial Imagination: How machines could learn creativity and common sense, among other human qualities", Scientific American, vol. 320, no. 5 (May 2019), pp. 58–63. Myers, Courtney Boyd ed. (2009). "The AI Report" Archived 29 July 2017 at the Wayback Machine. Forbes June 2009 Raphael, Bertram (1976). The Thinking Computer. W.H.Freeman and Company. ISBN 978-0-7167-0723-3. Archived from the original on 26 July 2020. Retrieved 22 August 2020. Scharre, Paul, "Killer Apps: The Real Dangers of an AI Arms Race", Foreign Affairs, vol. 98, no. 3 (May/June 2019), pp. 135–44. "Today's AI technologies are powerful but unreliable. Rules-based systems cannot deal with circumstances their programmers did not anticipate. Learning systems are limited by the data on which they were trained. AI failures have already led to tragedy. Advanced autopilot features in cars, although they perform well in some circumstances, have driven cars without warning into trucks, concrete barriers, and parked cars. In the wrong situation, AI systems go from supersmart to superdumb in an instant. When an enemy is trying to manipulate and hack an AI system, the risks are even greater." (p. 140.) Serenko, Alexander (2010). "The development of an AI journal ranking based on the revealed preference approach" (PDF). Journal of Informetrics. 4 (4): 447–459. doi:10.1016/j.joi.2010.04.001. Archived (PDF) from the original on 4 October 2013. Retrieved 24 August 2013. Serenko, Alexander; Michael Dohan (2011). "Comparing the expert survey and citation impact journal ranking methods: Example from the field of Artificial Intelligence" (PDF). Journal of Informetrics. 5 (4): 629–649. doi:10.1016/j.joi.2011.06.002. Archived (PDF) from the original on 4 October 2013. Retrieved 12 September 2013. Sun, R. & Bookman, L. (eds.), Computational Architectures: Integrating Neural and Symbolic Processes. Kluwer Academic Publishers, Needham, MA. 1994. Tom Simonite (29 December 2014). "2014 in Computing: Breakthroughs in Artificial Intelligence". MIT Technology Review. Tooze, Adam, "Democracy and Its Discontents", The New York Review of Books, vol. LXVI, no. 10 (6 June 2019), pp. 52–53, 56–57. "Democracy has no clear answer for the mindless operation of bureaucratic and technological power. We may indeed be witnessing its extension in the form of artificial intelligence and robotics. Likewise, after decades of dire warning, the environmental problem remains fundamentally unaddressed.... Bureaucratic overreach and environmental catastrophe are precisely the kinds of slow-moving existential challenges that democracies deal with very badly.... Finally, there is the threat du jour: corporations and the technologies they promote." (pp. 56–57.)

STATS305C: Applied Statistics III (Spring, 2023)

Implemention of the multiway bootstrap (including the Pigeonhole bootstrap, reweighting tensor bootstrap). Reweights observations with the product of weights for the units that observation is of (e.g., from crossed random effects). Owen, A.B., & Eckles, D. (2012). Bootstrapping data arrays of arbitrary order. Annals of Applied Statistics, 6(3), 895-927.

Material for STATS271: Applied Bayesian Statistics (Spring 2021)

Applied Environmental Statistics course: Boston University, Earth & Environment 509

:book: 现代应用统计 Modern Applied Statistics with R, INLA and Stan

Reference materials of "Probability & Statistics" IPE 205 course

Repository includes resources used in GEOG 4GA3 Applied Spatial Statistics

Resources associated with "Applied Multivariate Statistics in R" pressbook

Materials for HSE course "Applied Statistics in Machine Learning" taught during 2018.

My Masters of Applied Statistics Courses

Repo for the course Applied multivariate statistics

This repository contains course material for Applied Analytical Statistics 2021 (at Oxford Internet Institute, University of Oxford)

Code for the paper 'Variable Selection with Copula Entropy' published on Chinese Journal of Applied Probability and Statistics

GitBook of Note of Applied Statistics with R

Applied Statistics for High-Throughput Biology

Knowledgebase— a collection of information for quantitative finance, insurance, mathematics and AI—This serves as a sprawling notebook of books, papers, code links and Jupyter notebooks across quantitative finance, insurance, AI/ML, coding/IT, maths, probability and applied statistics, organised in subject-tree folders rather than a software repo.

Quickstart tutorial Prerequisites Before reading this tutorial you should know a bit of Python. If you would like to refresh your memory, take a look at the Python tutorial. If you wish to work the examples in this tutorial, you must also have some software installed on your computer. Please see https://scipy.org/install.html for instructions. Learner profile This tutorial is intended as a quick overview of algebra and arrays in NumPy and want to understand how n-dimensional (n>=2) arrays are represented and can be manipulated. In particular, if you don’t know how to apply common functions to n-dimensional arrays (without using for-loops), or if you want to understand axis and shape properties for n-dimensional arrays, this tutorial might be of help. Learning Objectives After this tutorial, you should be able to: Understand the difference between one-, two- and n-dimensional arrays in NumPy; Understand how to apply some linear algebra operations to n-dimensional arrays without using for-loops; Understand axis and shape properties for n-dimensional arrays. The Basics NumPy’s main object is the homogeneous multidimensional array. It is a table of elements (usually numbers), all of the same type, indexed by a tuple of non-negative integers. In NumPy dimensions are called axes. For example, the coordinates of a point in 3D space [1, 2, 1] has one axis. That axis has 3 elements in it, so we say it has a length of 3. In the example pictured below, the array has 2 axes. The first axis has a length of 2, the second axis has a length of 3. [[ 1., 0., 0.], [ 0., 1., 2.]] NumPy’s array class is called ndarray. It is also known by the alias array. Note that numpy.array is not the same as the Standard Python Library class array.array, which only handles one-dimensional arrays and offers less functionality. The more important attributes of an ndarray object are: ndarray.ndim the number of axes (dimensions) of the array. ndarray.shape the dimensions of the array. This is a tuple of integers indicating the size of the array in each dimension. For a matrix with n rows and m columns, shape will be (n,m). The length of the shape tuple is therefore the number of axes, ndim. ndarray.size the total number of elements of the array. This is equal to the product of the elements of shape. ndarray.dtype an object describing the type of the elements in the array. One can create or specify dtype’s using standard Python types. Additionally NumPy provides types of its own. numpy.int32, numpy.int16, and numpy.float64 are some examples. ndarray.itemsize the size in bytes of each element of the array. For example, an array of elements of type float64 has itemsize 8 (=64/8), while one of type complex32 has itemsize 4 (=32/8). It is equivalent to ndarray.dtype.itemsize. ndarray.data the buffer containing the actual elements of the array. Normally, we won’t need to use this attribute because we will access the elements in an array using indexing facilities. An example >>> import numpy as np a = np.arange(15).reshape(3, 5) a array([[ 0, 1, 2, 3, 4], [ 5, 6, 7, 8, 9], [10, 11, 12, 13, 14]]) a.shape (3, 5) a.ndim 2 a.dtype.name 'int64' a.itemsize 8 a.size 15 type(a) <class 'numpy.ndarray'> b = np.array([6, 7, 8]) b array([6, 7, 8]) type(b) <class 'numpy.ndarray'> Array Creation There are several ways to create arrays. For example, you can create an array from a regular Python list or tuple using the array function. The type of the resulting array is deduced from the type of the elements in the sequences. >>> >>> import numpy as np >>> a = np.array([2,3,4]) >>> a array([2, 3, 4]) >>> a.dtype dtype('int64') >>> b = np.array([1.2, 3.5, 5.1]) >>> b.dtype dtype('float64') A frequent error consists in calling array with multiple arguments, rather than providing a single sequence as an argument. >>> >>> a = np.array(1,2,3,4) # WRONG Traceback (most recent call last): ... TypeError: array() takes from 1 to 2 positional arguments but 4 were given >>> a = np.array([1,2,3,4]) # RIGHT array transforms sequences of sequences into two-dimensional arrays, sequences of sequences of sequences into three-dimensional arrays, and so on. >>> >>> b = np.array([(1.5,2,3), (4,5,6)]) >>> b array([[1.5, 2. , 3. ], [4. , 5. , 6. ]]) The type of the array can also be explicitly specified at creation time: >>> >>> c = np.array( [ [1,2], [3,4] ], dtype=complex ) >>> c array([[1.+0.j, 2.+0.j], [3.+0.j, 4.+0.j]]) Often, the elements of an array are originally unknown, but its size is known. Hence, NumPy offers several functions to create arrays with initial placeholder content. These minimize the necessity of growing arrays, an expensive operation. The function zeros creates an array full of zeros, the function ones creates an array full of ones, and the function empty creates an array whose initial content is random and depends on the state of the memory. By default, the dtype of the created array is float64. >>> >>> np.zeros((3, 4)) array([[0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.]]) >>> np.ones( (2,3,4), dtype=np.int16 ) # dtype can also be specified array([[[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]], [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]], dtype=int16) >>> np.empty( (2,3) ) # uninitialized array([[ 3.73603959e-262, 6.02658058e-154, 6.55490914e-260], # may vary [ 5.30498948e-313, 3.14673309e-307, 1.00000000e+000]]) To create sequences of numbers, NumPy provides the arange function which is analogous to the Python built-in range, but returns an array. >>> >>> np.arange( 10, 30, 5 ) array([10, 15, 20, 25]) >>> np.arange( 0, 2, 0.3 ) # it accepts float arguments array([0. , 0.3, 0.6, 0.9, 1.2, 1.5, 1.8]) When arange is used with floating point arguments, it is generally not possible to predict the number of elements obtained, due to the finite floating point precision. For this reason, it is usually better to use the function linspace that receives as an argument the number of elements that we want, instead of the step: >>> >>> from numpy import pi >>> np.linspace( 0, 2, 9 ) # 9 numbers from 0 to 2 array([0. , 0.25, 0.5 , 0.75, 1. , 1.25, 1.5 , 1.75, 2. ]) >>> x = np.linspace( 0, 2*pi, 100 ) # useful to evaluate function at lots of points >>> f = np.sin(x) See also array, zeros, zeros_like, ones, ones_like, empty, empty_like, arange, linspace, numpy.random.Generator.rand, numpy.random.Generator.randn, fromfunction, fromfile Printing Arrays When you print an array, NumPy displays it in a similar way to nested lists, but with the following layout: the last axis is printed from left to right, the second-to-last is printed from top to bottom, the rest are also printed from top to bottom, with each slice separated from the next by an empty line. One-dimensional arrays are then printed as rows, bidimensionals as matrices and tridimensionals as lists of matrices. >>> >>> a = np.arange(6) # 1d array >>> print(a) [0 1 2 3 4 5] >>> >>> b = np.arange(12).reshape(4,3) # 2d array >>> print(b) [[ 0 1 2] [ 3 4 5] [ 6 7 8] [ 9 10 11]] >>> >>> c = np.arange(24).reshape(2,3,4) # 3d array >>> print(c) [[[ 0 1 2 3] [ 4 5 6 7] [ 8 9 10 11]] [[12 13 14 15] [16 17 18 19] [20 21 22 23]]] See below to get more details on reshape. If an array is too large to be printed, NumPy automatically skips the central part of the array and only prints the corners: >>> >>> print(np.arange(10000)) [ 0 1 2 ... 9997 9998 9999] >>> >>> print(np.arange(10000).reshape(100,100)) [[ 0 1 2 ... 97 98 99] [ 100 101 102 ... 197 198 199] [ 200 201 202 ... 297 298 299] ... [9700 9701 9702 ... 9797 9798 9799] [9800 9801 9802 ... 9897 9898 9899] [9900 9901 9902 ... 9997 9998 9999]] To disable this behaviour and force NumPy to print the entire array, you can change the printing options using set_printoptions. >>> >>> np.set_printoptions(threshold=sys.maxsize) # sys module should be imported Basic Operations Arithmetic operators on arrays apply elementwise. A new array is created and filled with the result. >>> >>> a = np.array( [20,30,40,50] ) >>> b = np.arange( 4 ) >>> b array([0, 1, 2, 3]) >>> c = a-b >>> c array([20, 29, 38, 47]) >>> b**2 array([0, 1, 4, 9]) >>> 10*np.sin(a) array([ 9.12945251, -9.88031624, 7.4511316 , -2.62374854]) >>> a<35 array([ True, True, False, False]) Unlike in many matrix languages, the product operator * operates elementwise in NumPy arrays. The matrix product can be performed using the @ operator (in python >=3.5) or the dot function or method: >>> >>> A = np.array( [[1,1], ... [0,1]] ) >>> B = np.array( [[2,0], ... [3,4]] ) >>> A * B # elementwise product array([[2, 0], [0, 4]]) >>> A @ B # matrix product array([[5, 4], [3, 4]]) >>> A.dot(B) # another matrix product array([[5, 4], [3, 4]]) Some operations, such as += and *=, act in place to modify an existing array rather than create a new one. >>> >>> rg = np.random.default_rng(1) # create instance of default random number generator >>> a = np.ones((2,3), dtype=int) >>> b = rg.random((2,3)) >>> a *= 3 >>> a array([[3, 3, 3], [3, 3, 3]]) >>> b += a >>> b array([[3.51182162, 3.9504637 , 3.14415961], [3.94864945, 3.31183145, 3.42332645]]) >>> a += b # b is not automatically converted to integer type Traceback (most recent call last): ... numpy.core._exceptions.UFuncTypeError: Cannot cast ufunc 'add' output from dtype('float64') to dtype('int64') with casting rule 'same_kind' When operating with arrays of different types, the type of the resulting array corresponds to the more general or precise one (a behavior known as upcasting). >>> >>> a = np.ones(3, dtype=np.int32) >>> b = np.linspace(0,pi,3) >>> b.dtype.name 'float64' >>> c = a+b >>> c array([1. , 2.57079633, 4.14159265]) >>> c.dtype.name 'float64' >>> d = np.exp(c*1j) >>> d array([ 0.54030231+0.84147098j, -0.84147098+0.54030231j, -0.54030231-0.84147098j]) >>> d.dtype.name 'complex128' Many unary operations, such as computing the sum of all the elements in the array, are implemented as methods of the ndarray class. >>> >>> a = rg.random((2,3)) >>> a array([[0.82770259, 0.40919914, 0.54959369], [0.02755911, 0.75351311, 0.53814331]]) >>> a.sum() 3.1057109529998157 >>> a.min() 0.027559113243068367 >>> a.max() 0.8277025938204418 By default, these operations apply to the array as though it were a list of numbers, regardless of its shape. However, by specifying the axis parameter you can apply an operation along the specified axis of an array: >>> >>> b = np.arange(12).reshape(3,4) >>> b array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]]) >>> >>> b.sum(axis=0) # sum of each column array([12, 15, 18, 21]) >>> >>> b.min(axis=1) # min of each row array([0, 4, 8]) >>> >>> b.cumsum(axis=1) # cumulative sum along each row array([[ 0, 1, 3, 6], [ 4, 9, 15, 22], [ 8, 17, 27, 38]]) Universal Functions NumPy provides familiar mathematical functions such as sin, cos, and exp. In NumPy, these are called “universal functions”(ufunc). Within NumPy, these functions operate elementwise on an array, producing an array as output. >>> >>> B = np.arange(3) >>> B array([0, 1, 2]) >>> np.exp(B) array([1. , 2.71828183, 7.3890561 ]) >>> np.sqrt(B) array([0. , 1. , 1.41421356]) >>> C = np.array([2., -1., 4.]) >>> np.add(B, C) array([2., 0., 6.]) See also all, any, apply_along_axis, argmax, argmin, argsort, average, bincount, ceil, clip, conj, corrcoef, cov, cross, cumprod, cumsum, diff, dot, floor, inner, invert, lexsort, max, maximum, mean, median, min, minimum, nonzero, outer, prod, re, round, sort, std, sum, trace, transpose, var, vdot, vectorize, where Indexing, Slicing and Iterating One-dimensional arrays can be indexed, sliced and iterated over, much like lists and other Python sequences. >>> >>> a = np.arange(10)**3 >>> a array([ 0, 1, 8, 27, 64, 125, 216, 343, 512, 729]) >>> a[2] 8 >>> a[2:5] array([ 8, 27, 64]) # equivalent to a[0:6:2] = 1000; # from start to position 6, exclusive, set every 2nd element to 1000 >>> a[:6:2] = 1000 >>> a array([1000, 1, 1000, 27, 1000, 125, 216, 343, 512, 729]) >>> a[ : :-1] # reversed a array([ 729, 512, 343, 216, 125, 1000, 27, 1000, 1, 1000]) >>> for i in a: ... print(i**(1/3.)) ... 9.999999999999998 1.0 9.999999999999998 3.0 9.999999999999998 4.999999999999999 5.999999999999999 6.999999999999999 7.999999999999999 8.999999999999998 Multidimensional arrays can have one index per axis. These indices are given in a tuple separated by commas: >>> >>> def f(x,y): ... return 10*x+y ... >>> b = np.fromfunction(f,(5,4),dtype=int) >>> b array([[ 0, 1, 2, 3], [10, 11, 12, 13], [20, 21, 22, 23], [30, 31, 32, 33], [40, 41, 42, 43]]) >>> b[2,3] 23 >>> b[0:5, 1] # each row in the second column of b array([ 1, 11, 21, 31, 41]) >>> b[ : ,1] # equivalent to the previous example array([ 1, 11, 21, 31, 41]) >>> b[1:3, : ] # each column in the second and third row of b array([[10, 11, 12, 13], [20, 21, 22, 23]]) When fewer indices are provided than the number of axes, the missing indices are considered complete slices: >>> >>> b[-1] # the last row. Equivalent to b[-1,:] array([40, 41, 42, 43]) The expression within brackets in b[i] is treated as an i followed by as many instances of : as needed to represent the remaining axes. NumPy also allows you to write this using dots as b[i,...]. The dots (...) represent as many colons as needed to produce a complete indexing tuple. For example, if x is an array with 5 axes, then x[1,2,...] is equivalent to x[1,2,:,:,:], x[...,3] to x[:,:,:,:,3] and x[4,...,5,:] to x[4,:,:,5,:]. >>> >>> c = np.array( [[[ 0, 1, 2], # a 3D array (two stacked 2D arrays) ... [ 10, 12, 13]], ... [[100,101,102], ... [110,112,113]]]) >>> c.shape (2, 2, 3) >>> c[1,...] # same as c[1,:,:] or c[1] array([[100, 101, 102], [110, 112, 113]]) >>> c[...,2] # same as c[:,:,2] array([[ 2, 13], [102, 113]]) Iterating over multidimensional arrays is done with respect to the first axis: >>> >>> for row in b: ... print(row) ... [0 1 2 3] [10 11 12 13] [20 21 22 23] [30 31 32 33] [40 41 42 43] However, if one wants to perform an operation on each element in the array, one can use the flat attribute which is an iterator over all the elements of the array: >>> >>> for element in b.flat: ... print(element) ... 0 1 2 3 10 11 12 13 20 21 22 23 30 31 32 33 40 41 42 43 See also Indexing, Indexing (reference), newaxis, ndenumerate, indices Shape Manipulation Changing the shape of an array An array has a shape given by the number of elements along each axis: >>> >>> a = np.floor(10*rg.random((3,4))) >>> a array([[3., 7., 3., 4.], [1., 4., 2., 2.], [7., 2., 4., 9.]]) >>> a.shape (3, 4) The shape of an array can be changed with various commands. Note that the following three commands all return a modified array, but do not change the original array: >>> >>> a.ravel() # returns the array, flattened array([3., 7., 3., 4., 1., 4., 2., 2., 7., 2., 4., 9.]) >>> a.reshape(6,2) # returns the array with a modified shape array([[3., 7.], [3., 4.], [1., 4.], [2., 2.], [7., 2.], [4., 9.]]) >>> a.T # returns the array, transposed array([[3., 1., 7.], [7., 4., 2.], [3., 2., 4.], [4., 2., 9.]]) >>> a.T.shape (4, 3) >>> a.shape (3, 4) The order of the elements in the array resulting from ravel() is normally “C-style”, that is, the rightmost index “changes the fastest”, so the element after a[0,0] is a[0,1]. If the array is reshaped to some other shape, again the array is treated as “C-style”. NumPy normally creates arrays stored in this order, so ravel() will usually not need to copy its argument, but if the array was made by taking slices of another array or created with unusual options, it may need to be copied. The functions ravel() and reshape() can also be instructed, using an optional argument, to use FORTRAN-style arrays, in which the leftmost index changes the fastest. The reshape function returns its argument with a modified shape, whereas the ndarray.resize method modifies the array itself: >>> >>> a array([[3., 7., 3., 4.], [1., 4., 2., 2.], [7., 2., 4., 9.]]) >>> a.resize((2,6)) >>> a array([[3., 7., 3., 4., 1., 4.], [2., 2., 7., 2., 4., 9.]]) If a dimension is given as -1 in a reshaping operation, the other dimensions are automatically calculated: >>> >>> a.reshape(3,-1) array([[3., 7., 3., 4.], [1., 4., 2., 2.], [7., 2., 4., 9.]]) See also ndarray.shape, reshape, resize, ravel Stacking together different arrays Several arrays can be stacked together along different axes: >>> >>> a = np.floor(10*rg.random((2,2))) >>> a array([[9., 7.], [5., 2.]]) >>> b = np.floor(10*rg.random((2,2))) >>> b array([[1., 9.], [5., 1.]]) >>> np.vstack((a,b)) array([[9., 7.], [5., 2.], [1., 9.], [5., 1.]]) >>> np.hstack((a,b)) array([[9., 7., 1., 9.], [5., 2., 5., 1.]]) The function column_stack stacks 1D arrays as columns into a 2D array. It is equivalent to hstack only for 2D arrays: >>> >>> from numpy import newaxis >>> np.column_stack((a,b)) # with 2D arrays array([[9., 7., 1., 9.], [5., 2., 5., 1.]]) >>> a = np.array([4.,2.]) >>> b = np.array([3.,8.]) >>> np.column_stack((a,b)) # returns a 2D array array([[4., 3.], [2., 8.]]) >>> np.hstack((a,b)) # the result is different array([4., 2., 3., 8.]) >>> a[:,newaxis] # view `a` as a 2D column vector array([[4.], [2.]]) >>> np.column_stack((a[:,newaxis],b[:,newaxis])) array([[4., 3.], [2., 8.]]) >>> np.hstack((a[:,newaxis],b[:,newaxis])) # the result is the same array([[4., 3.], [2., 8.]]) On the other hand, the function row_stack is equivalent to vstack for any input arrays. In fact, row_stack is an alias for vstack: >>> >>> np.column_stack is np.hstack False >>> np.row_stack is np.vstack True In general, for arrays with more than two dimensions, hstack stacks along their second axes, vstack stacks along their first axes, and concatenate allows for an optional arguments giving the number of the axis along which the concatenation should happen. Note In complex cases, r_ and c_ are useful for creating arrays by stacking numbers along one axis. They allow the use of range literals (“:”) >>> >>> np.r_[1:4,0,4] array([1, 2, 3, 0, 4]) When used with arrays as arguments, r_ and c_ are similar to vstack and hstack in their default behavior, but allow for an optional argument giving the number of the axis along which to concatenate. See also hstack, vstack, column_stack, concatenate, c_, r_ Splitting one array into several smaller ones Using hsplit, you can split an array along its horizontal axis, either by specifying the number of equally shaped arrays to return, or by specifying the columns after which the division should occur: >>> >>> a = np.floor(10*rg.random((2,12))) >>> a array([[6., 7., 6., 9., 0., 5., 4., 0., 6., 8., 5., 2.], [8., 5., 5., 7., 1., 8., 6., 7., 1., 8., 1., 0.]]) # Split a into 3 >>> np.hsplit(a,3) [array([[6., 7., 6., 9.], [8., 5., 5., 7.]]), array([[0., 5., 4., 0.], [1., 8., 6., 7.]]), array([[6., 8., 5., 2.], [1., 8., 1., 0.]])] # Split a after the third and the fourth column >>> np.hsplit(a,(3,4)) [array([[6., 7., 6.], [8., 5., 5.]]), array([[9.], [7.]]), array([[0., 5., 4., 0., 6., 8., 5., 2.], [1., 8., 6., 7., 1., 8., 1., 0.]])] vsplit splits along the vertical axis, and array_split allows one to specify along which axis to split. Copies and Views When operating and manipulating arrays, their data is sometimes copied into a new array and sometimes not. This is often a source of confusion for beginners. There are three cases: No Copy at All Simple assignments make no copy of objects or their data. >>> >>> a = np.array([[ 0, 1, 2, 3], ... [ 4, 5, 6, 7], ... [ 8, 9, 10, 11]]) >>> b = a # no new object is created >>> b is a # a and b are two names for the same ndarray object True Python passes mutable objects as references, so function calls make no copy. >>> >>> def f(x): ... print(id(x)) ... >>> id(a) # id is a unique identifier of an object 148293216 # may vary >>> f(a) 148293216 # may vary View or Shallow Copy Different array objects can share the same data. The view method creates a new array object that looks at the same data. >>> >>> c = a.view() >>> c is a False >>> c.base is a # c is a view of the data owned by a True >>> c.flags.owndata False >>> >>> c = c.reshape((2, 6)) # a's shape doesn't change >>> a.shape (3, 4) >>> c[0, 4] = 1234 # a's data changes >>> a array([[ 0, 1, 2, 3], [1234, 5, 6, 7], [ 8, 9, 10, 11]]) Slicing an array returns a view of it: >>> >>> s = a[ : , 1:3] # spaces added for clarity; could also be written "s = a[:, 1:3]" >>> s[:] = 10 # s[:] is a view of s. Note the difference between s = 10 and s[:] = 10 >>> a array([[ 0, 10, 10, 3], [1234, 10, 10, 7], [ 8, 10, 10, 11]]) Deep Copy The copy method makes a complete copy of the array and its data. >>> >>> d = a.copy() # a new array object with new data is created >>> d is a False >>> d.base is a # d doesn't share anything with a False >>> d[0,0] = 9999 >>> a array([[ 0, 10, 10, 3], [1234, 10, 10, 7], [ 8, 10, 10, 11]]) Sometimes copy should be called after slicing if the original array is not required anymore. For example, suppose a is a huge intermediate result and the final result b only contains a small fraction of a, a deep copy should be made when constructing b with slicing: >>> >>> a = np.arange(int(1e8)) >>> b = a[:100].copy() >>> del a # the memory of ``a`` can be released. If b = a[:100] is used instead, a is referenced by b and will persist in memory even if del a is executed. Functions and Methods Overview Here is a list of some useful NumPy functions and methods names ordered in categories. See Routines for the full list. Array Creation arange, array, copy, empty, empty_like, eye, fromfile, fromfunction, identity, linspace, logspace, mgrid, ogrid, ones, ones_like, r_, zeros, zeros_like Conversions ndarray.astype, atleast_1d, atleast_2d, atleast_3d, mat Manipulations array_split, column_stack, concatenate, diagonal, dsplit, dstack, hsplit, hstack, ndarray.item, newaxis, ravel, repeat, reshape, resize, squeeze, swapaxes, take, transpose, vsplit, vstack Questions all, any, nonzero, where Ordering argmax, argmin, argsort, max, min, ptp, searchsorted, sort Operations choose, compress, cumprod, cumsum, inner, ndarray.fill, imag, prod, put, putmask, real, sum Basic Statistics cov, mean, std, var Basic Linear Algebra cross, dot, outer, linalg.svd, vdot Less Basic Broadcasting rules Broadcasting allows universal functions to deal in a meaningful way with inputs that do not have exactly the same shape. The first rule of broadcasting is that if all input arrays do not have the same number of dimensions, a “1” will be repeatedly prepended to the shapes of the smaller arrays until all the arrays have the same number of dimensions. The second rule of broadcasting ensures that arrays with a size of 1 along a particular dimension act as if they had the size of the array with the largest shape along that dimension. The value of the array element is assumed to be the same along that dimension for the “broadcast” array. After application of the broadcasting rules, the sizes of all arrays must match. More details can be found in Broadcasting. Advanced indexing and index tricks NumPy offers more indexing facilities than regular Python sequences. In addition to indexing by integers and slices, as we saw before, arrays can be indexed by arrays of integers and arrays of booleans. Indexing with Arrays of Indices >>> >>> a = np.arange(12)**2 # the first 12 square numbers >>> i = np.array([1, 1, 3, 8, 5]) # an array of indices >>> a[i] # the elements of a at the positions i array([ 1, 1, 9, 64, 25]) >>> >>> j = np.array([[3, 4], [9, 7]]) # a bidimensional array of indices >>> a[j] # the same shape as j array([[ 9, 16], [81, 49]]) When the indexed array a is multidimensional, a single array of indices refers to the first dimension of a. The following example shows this behavior by converting an image of labels into a color image using a palette. >>> >>> palette = np.array([[0, 0, 0], # black ... [255, 0, 0], # red ... [0, 255, 0], # green ... [0, 0, 255], # blue ... [255, 255, 255]]) # white >>> image = np.array([[0, 1, 2, 0], # each value corresponds to a color in the palette ... [0, 3, 4, 0]]) >>> palette[image] # the (2, 4, 3) color image array([[[ 0, 0, 0], [255, 0, 0], [ 0, 255, 0], [ 0, 0, 0]], [[ 0, 0, 0], [ 0, 0, 255], [255, 255, 255], [ 0, 0, 0]]]) We can also give indexes for more than one dimension. The arrays of indices for each dimension must have the same shape. >>> >>> a = np.arange(12).reshape(3,4) >>> a array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]]) >>> i = np.array([[0, 1], # indices for the first dim of a ... [1, 2]]) >>> j = np.array([[2, 1], # indices for the second dim ... [3, 3]]) >>> >>> a[i, j] # i and j must have equal shape array([[ 2, 5], [ 7, 11]]) >>> >>> a[i, 2] array([[ 2, 6], [ 6, 10]]) >>> >>> a[:, j] # i.e., a[ : , j] array([[[ 2, 1], [ 3, 3]], [[ 6, 5], [ 7, 7]], [[10, 9], [11, 11]]]) In Python, arr[i, j] is exactly the same as arr[(i, j)]—so we can put i and j in a tuple and then do the indexing with that. >>> >>> l = (i, j) # equivalent to a[i, j] >>> a[l] array([[ 2, 5], [ 7, 11]]) However, we can not do this by putting i and j into an array, because this array will be interpreted as indexing the first dimension of a. >>> >>> s = np.array([i, j]) # not what we want >>> a[s] Traceback (most recent call last): File "<stdin>", line 1, in <module> IndexError: index 3 is out of bounds for axis 0 with size 3 # same as a[i, j] >>> a[tuple(s)] array([[ 2, 5], [ 7, 11]]) Another common use of indexing with arrays is the search of the maximum value of time-dependent series: >>> >>> time = np.linspace(20, 145, 5) # time scale >>> data = np.sin(np.arange(20)).reshape(5,4) # 4 time-dependent series >>> time array([ 20. , 51.25, 82.5 , 113.75, 145. ]) >>> data array([[ 0. , 0.84147098, 0.90929743, 0.14112001], [-0.7568025 , -0.95892427, -0.2794155 , 0.6569866 ], [ 0.98935825, 0.41211849, -0.54402111, -0.99999021], [-0.53657292, 0.42016704, 0.99060736, 0.65028784], [-0.28790332, -0.96139749, -0.75098725, 0.14987721]]) # index of the maxima for each series >>> ind = data.argmax(axis=0) >>> ind array([2, 0, 3, 1]) # times corresponding to the maxima >>> time_max = time[ind] >>> >>> data_max = data[ind, range(data.shape[1])] # => data[ind[0],0], data[ind[1],1]... >>> time_max array([ 82.5 , 20. , 113.75, 51.25]) >>> data_max array([0.98935825, 0.84147098, 0.99060736, 0.6569866 ]) >>> np.all(data_max == data.max(axis=0)) True You can also use indexing with arrays as a target to assign to: >>> >>> a = np.arange(5) >>> a array([0, 1, 2, 3, 4]) >>> a[[1,3,4]] = 0 >>> a array([0, 0, 2, 0, 0]) However, when the list of indices contains repetitions, the assignment is done several times, leaving behind the last value: >>> >>> a = np.arange(5) >>> a[[0,0,2]]=[1,2,3] >>> a array([2, 1, 3, 3, 4]) This is reasonable enough, but watch out if you want to use Python’s += construct, as it may not do what you expect: >>> >>> a = np.arange(5) >>> a[[0,0,2]]+=1 >>> a array([1, 1, 3, 3, 4]) Even though 0 occurs twice in the list of indices, the 0th element is only incremented once. This is because Python requires “a+=1” to be equivalent to “a = a + 1”. Indexing with Boolean Arrays When we index arrays with arrays of (integer) indices we are providing the list of indices to pick. With boolean indices the approach is different; we explicitly choose which items in the array we want and which ones we don’t. The most natural way one can think of for boolean indexing is to use boolean arrays that have the same shape as the original array: >>> >>> a = np.arange(12).reshape(3,4) >>> b = a > 4 >>> b # b is a boolean with a's shape array([[False, False, False, False], [False, True, True, True], [ True, True, True, True]]) >>> a[b] # 1d array with the selected elements array([ 5, 6, 7, 8, 9, 10, 11]) This property can be very useful in assignments: >>> >>> a[b] = 0 # All elements of 'a' higher than 4 become 0 >>> a array([[0, 1, 2, 3], [4, 0, 0, 0], [0, 0, 0, 0]]) You can look at the following example to see how to use boolean indexing to generate an image of the Mandelbrot set: >>> import numpy as np import matplotlib.pyplot as plt def mandelbrot( h,w, maxit=20 ): """Returns an image of the Mandelbrot fractal of size (h,w).""" y,x = np.ogrid[ -1.4:1.4:h*1j, -2:0.8:w*1j ] c = x+y*1j z = c divtime = maxit + np.zeros(z.shape, dtype=int) for i in range(maxit): z = z**2 + c diverge = z*np.conj(z) > 2**2 # who is diverging div_now = diverge & (divtime==maxit) # who is diverging now divtime[div_now] = i # note when z[diverge] = 2 # avoid diverging too much return divtime plt.imshow(mandelbrot(400,400)) ../_images/quickstart-1.png The second way of indexing with booleans is more similar to integer indexing; for each dimension of the array we give a 1D boolean array selecting the slices we want: >>> >>> a = np.arange(12).reshape(3,4) >>> b1 = np.array([False,True,True]) # first dim selection >>> b2 = np.array([True,False,True,False]) # second dim selection >>> >>> a[b1,:] # selecting rows array([[ 4, 5, 6, 7], [ 8, 9, 10, 11]]) >>> >>> a[b1] # same thing array([[ 4, 5, 6, 7], [ 8, 9, 10, 11]]) >>> >>> a[:,b2] # selecting columns array([[ 0, 2], [ 4, 6], [ 8, 10]]) >>> >>> a[b1,b2] # a weird thing to do array([ 4, 10]) Note that the length of the 1D boolean array must coincide with the length of the dimension (or axis) you want to slice. In the previous example, b1 has length 3 (the number of rows in a), and b2 (of length 4) is suitable to index the 2nd axis (columns) of a. The ix_() function The ix_ function can be used to combine different vectors so as to obtain the result for each n-uplet. For example, if you want to compute all the a+b*c for all the triplets taken from each of the vectors a, b and c: >>> >>> a = np.array([2,3,4,5]) >>> b = np.array([8,5,4]) >>> c = np.array([5,4,6,8,3]) >>> ax,bx,cx = np.ix_(a,b,c) >>> ax array([[[2]], [[3]], [[4]], [[5]]]) >>> bx array([[[8], [5], [4]]]) >>> cx array([[[5, 4, 6, 8, 3]]]) >>> ax.shape, bx.shape, cx.shape ((4, 1, 1), (1, 3, 1), (1, 1, 5)) >>> result = ax+bx*cx >>> result array([[[42, 34, 50, 66, 26], [27, 22, 32, 42, 17], [22, 18, 26, 34, 14]], [[43, 35, 51, 67, 27], [28, 23, 33, 43, 18], [23, 19, 27, 35, 15]], [[44, 36, 52, 68, 28], [29, 24, 34, 44, 19], [24, 20, 28, 36, 16]], [[45, 37, 53, 69, 29], [30, 25, 35, 45, 20], [25, 21, 29, 37, 17]]]) >>> result[3,2,4] 17 >>> a[3]+b[2]*c[4] 17 You could also implement the reduce as follows: >>> >>> def ufunc_reduce(ufct, *vectors): ... vs = np.ix_(*vectors) ... r = ufct.identity ... for v in vs: ... r = ufct(r,v) ... return r and then use it as: >>> >>> ufunc_reduce(np.add,a,b,c) array([[[15, 14, 16, 18, 13], [12, 11, 13, 15, 10], [11, 10, 12, 14, 9]], [[16, 15, 17, 19, 14], [13, 12, 14, 16, 11], [12, 11, 13, 15, 10]], [[17, 16, 18, 20, 15], [14, 13, 15, 17, 12], [13, 12, 14, 16, 11]], [[18, 17, 19, 21, 16], [15, 14, 16, 18, 13], [14, 13, 15, 17, 12]]]) The advantage of this version of reduce compared to the normal ufunc.reduce is that it makes use of the Broadcasting Rules in order to avoid creating an argument array the size of the output times the number of vectors. Indexing with strings See Structured arrays. Linear Algebra Work in progress. Basic linear algebra to be included here. Simple Array Operations See linalg.py in numpy folder for more. >>> >>> import numpy as np >>> a = np.array([[1.0, 2.0], [3.0, 4.0]]) >>> print(a) [[1. 2.] [3. 4.]] >>> a.transpose() array([[1., 3.], [2., 4.]]) >>> np.linalg.inv(a) array([[-2. , 1. ], [ 1.5, -0.5]]) >>> u = np.eye(2) # unit 2x2 matrix; "eye" represents "I" >>> u array([[1., 0.], [0., 1.]]) >>> j = np.array([[0.0, -1.0], [1.0, 0.0]]) >>> j @ j # matrix product array([[-1., 0.], [ 0., -1.]]) >>> np.trace(u) # trace 2.0 >>> y = np.array([[5.], [7.]]) >>> np.linalg.solve(a, y) array([[-3.], [ 4.]]) >>> np.linalg.eig(j) (array([0.+1.j, 0.-1.j]), array([[0.70710678+0.j , 0.70710678-0.j ], [0. -0.70710678j, 0. +0.70710678j]])) Parameters: square matrix Returns The eigenvalues, each repeated according to its multiplicity. The normalized (unit "length") eigenvectors, such that the column ``v[:,i]`` is the eigenvector corresponding to the eigenvalue ``w[i]`` . Tricks and Tips Here we give a list of short and useful tips. “Automatic” Reshaping To change the dimensions of an array, you can omit one of the sizes which will then be deduced automatically: >>> >>> a = np.arange(30) >>> b = a.reshape((2, -1, 3)) # -1 means "whatever is needed" >>> b.shape (2, 5, 3) >>> b array([[[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11], [12, 13, 14]], [[15, 16, 17], [18, 19, 20], [21, 22, 23], [24, 25, 26], [27, 28, 29]]]) Vector Stacking How do we construct a 2D array from a list of equally-sized row vectors? In MATLAB this is quite easy: if x and y are two vectors of the same length you only need do m=[x;y]. In NumPy this works via the functions column_stack, dstack, hstack and vstack, depending on the dimension in which the stacking is to be done. For example: >>> >>> x = np.arange(0,10,2) >>> y = np.arange(5) >>> m = np.vstack([x,y]) >>> m array([[0, 2, 4, 6, 8], [0, 1, 2, 3, 4]]) >>> xy = np.hstack([x,y]) >>> xy array([0, 2, 4, 6, 8, 0, 1, 2, 3, 4]) The logic behind those functions in more than two dimensions can be strange. See also NumPy for Matlab users Histograms The NumPy histogram function applied to an array returns a pair of vectors: the histogram of the array and a vector of the bin edges. Beware: matplotlib also has a function to build histograms (called hist, as in Matlab) that differs from the one in NumPy. The main difference is that pylab.hist plots the histogram automatically, while numpy.histogram only generates the data. >>> import numpy as np rg = np.random.default_rng(1) import matplotlib.pyplot as plt # Build a vector of 10000 normal deviates with variance 0.5^2 and mean 2 mu, sigma = 2, 0.5 v = rg.normal(mu,sigma,10000) # Plot a normalized histogram with 50 bins plt.hist(v, bins=50, density=1) # matplotlib version (plot) # Compute the histogram with numpy and then plot it (n, bins) = np.histogram(v, bins=50, density=True) # NumPy version (no plot) plt.plot(.5*(bins[1:]+bins[:-1]), n) ../_images/quickstart-2.png Further reading The Python tutorial NumPy Reference SciPy Tutorial SciPy Lecture Notes A matlab, R, IDL, NumPy/SciPy dictionary © Copyright 2008-2020, The SciPy community. Last updated on Jun 29, 2020. Created using Sphinx 2.4.4.

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