Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG

Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models

Mathematics for Machine Learning and Data Science Specialization - Coursera - deeplearning.ai - solutions and notes

This is the notes and code I took while studying an NLP tutorial [2019 Latest AI Natural Language Processing Deep Machine Learning Top Project Practical Course]

Notes for Machine Learning Engineering for Production (MLOps) Specialization course by DeepLearning.AI & Andrew Ng

These are my notes which I prepared during deep learning specialization taught by AI guru Andrew NG. I have used diagrams and code snippets from the code whenever needed but following The Honor Code.

using Neural Networks (SSD) on Tensorflow. This repo documents steps and scripts used to train a hand detector using Tensorflow (Object Detection API). As with any DNN based task, the most expensive (and riskiest) part of the process has to do with finding or creating the right (annotated) dataset. I was interested mainly in detecting hands on a table (egocentric view point). I experimented first with the [Oxford Hands Dataset](http://www.robots.ox.ac.uk/~vgg/data/hands/) (the results were not good). I then tried the [Egohands Dataset](http://vision.soic.indiana.edu/projects/egohands/) which was a much better fit to my requirements. The goal of this repo/post is to demonstrate how neural networks can be applied to the (hard) problem of tracking hands (egocentric and other views). Better still, provide code that can be adapted to other uses cases. If you use this tutorial or models in your research or project, please cite [this](#citing-this-tutorial). Here is the detector in action. <img src="images/hand1.gif" width="33.3%"><img src="images/hand2.gif" width="33.3%"><img src="images/hand3.gif" width="33.3%"> Realtime detection on video stream from a webcam . <img src="images/chess1.gif" width="33.3%"><img src="images/chess2.gif" width="33.3%"><img src="images/chess3.gif" width="33.3%"> Detection on a Youtube video. Both examples above were run on a macbook pro **CPU** (i7, 2.5GHz, 16GB). Some fps numbers are: | FPS | Image Size | Device| Comments| | ------------- | ------------- | ------------- | ------------- | | 21 | 320 * 240 | Macbook pro (i7, 2.5GHz, 16GB) | Run without visualizing results| | 16 | 320 * 240 | Macbook pro (i7, 2.5GHz, 16GB) | Run while visualizing results (image above) | | 11 | 640 * 480 | Macbook pro (i7, 2.5GHz, 16GB) | Run while visualizing results (image above) | > Note: The code in this repo is written and tested with Tensorflow `1.4.0-rc0`. Using a different version may result in [some errors](https://github.com/tensorflow/models/issues/1581). You may need to [generate your own frozen model](https://pythonprogramming.net/testing-custom-object-detector-tensorflow-object-detection-api-tutorial/?completed=/training-custom-objects-tensorflow-object-detection-api-tutorial/) graph using the [model checkpoints](model-checkpoint) in the repo to fit your TF version. **Content of this document** - Motivation - Why Track/Detect hands with Neural Networks - Data preparation and network training in Tensorflow (Dataset, Import, Training) - Training the hand detection Model - Using the Detector to Detect/Track hands - Thoughts on Optimizations. > P.S if you are using or have used the models provided here, feel free to reach out on twitter ([@vykthur](https://twitter.com/vykthur)) and share your work! ## Motivation - Why Track/Detect hands with Neural Networks? There are several existing approaches to tracking hands in the computer vision domain. Incidentally, many of these approaches are rule based (e.g extracting background based on texture and boundary features, distinguishing between hands and background using color histograms and HOG classifiers,) making them not very robust. For example, these algorithms might get confused if the background is unusual or in situations where sharp changes in lighting conditions cause sharp changes in skin color or the tracked object becomes occluded.(see [here for a review](https://www.cse.unr.edu/~bebis/handposerev.pdf) paper on hand pose estimation from the HCI perspective) With sufficiently large datasets, neural networks provide opportunity to train models that perform well and address challenges of existing object tracking/detection algorithms - varied/poor lighting, noisy environments, diverse viewpoints and even occlusion. The main drawbacks to usage for real-time tracking/detection is that they can be complex, are relatively slow compared to tracking-only algorithms and it can be quite expensive to assemble a good dataset. But things are changing with advances in fast neural networks. Furthermore, this entire area of work has been made more approachable by deep learning frameworks (such as the tensorflow object detection api) that simplify the process of training a model for custom object detection. More importantly, the advent of fast neural network models like ssd, faster r-cnn, rfcn (see [here](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md#coco-trained-models-coco-models) ) etc make neural networks an attractive candidate for real-time detection (and tracking) applications. Hopefully, this repo demonstrates this. > If you are not interested in the process of training the detector, you can skip straight to applying the [pretrained model I provide in detecting hands](#detecting-hands). Training a model is a multi-stage process (assembling dataset, cleaning, splitting into training/test partitions and generating an inference graph). While I lightly touch on the details of these parts, there are a few other tutorials cover training a custom object detector using the tensorflow object detection api in more detail[ see [here](https://pythonprogramming.net/training-custom-objects-tensorflow-object-detection-api-tutorial/) and [here](https://towardsdatascience.com/how-to-train-your-own-object-detector-with-tensorflows-object-detector-api-bec72ecfe1d9) ]. I recommend you walk through those if interested in training a custom object detector from scratch. ## Data preparation and network training in Tensorflow (Dataset, Import, Training) **The Egohands Dataset** The hand detector model is built using data from the [Egohands Dataset](http://vision.soic.indiana.edu/projects/egohands/) dataset. This dataset works well for several reasons. It contains high quality, pixel level annotations (>15000 ground truth labels) where hands are located across 4800 images. All images are captured from an egocentric view (Google glass) across 48 different environments (indoor, outdoor) and activities (playing cards, chess, jenga, solving puzzles etc). <img src="images/egohandstrain.jpg" width="100%"> If you will be using the Egohands dataset, you can cite them as follows: > Bambach, Sven, et al. "Lending a hand: Detecting hands and recognizing activities in complex egocentric interactions." Proceedings of the IEEE International Conference on Computer Vision. 2015. The Egohands dataset (zip file with labelled data) contains 48 folders of locations where video data was collected (100 images per folder). ``` -- LOCATION_X -- frame_1.jpg -- frame_2.jpg ... -- frame_100.jpg -- polygons.mat // contains annotations for all 100 images in current folder -- LOCATION_Y -- frame_1.jpg -- frame_2.jpg ... -- frame_100.jpg -- polygons.mat // contains annotations for all 100 images in current folder ``` **Converting data to Tensorflow Format** Some initial work needs to be done to the Egohands dataset to transform it into the format (`tfrecord`) which Tensorflow needs to train a model. This repo contains `egohands_dataset_clean.py` a script that will help you generate these csv files. - Downloads the egohands datasets - Renames all files to include their directory names to ensure each filename is unique - Splits the dataset into train (80%), test (10%) and eval (10%) folders. - Reads in `polygons.mat` for each folder, generates bounding boxes and visualizes them to ensure correctness (see image above). - Once the script is done running, you should have an images folder containing three folders - train, test and eval. Each of these folders should also contain a csv label document each - `train_labels.csv`, `test_labels.csv` that can be used to generate `tfrecords` Note: While the egohands dataset provides four separate labels for hands (own left, own right, other left, and other right), for my purpose, I am only interested in the general `hand` class and label all training data as `hand`. You can modify the data prep script to generate `tfrecords` that support 4 labels. Next: convert your dataset + csv files to tfrecords. A helpful guide on this can be found [here](https://pythonprogramming.net/creating-tfrecord-files-tensorflow-object-detection-api-tutorial/).For each folder, you should be able to generate `train.record`, `test.record` required in the training process. ## Training the hand detection Model Now that the dataset has been assembled (and your tfrecords), the next task is to train a model based on this. With neural networks, it is possible to use a process called [transfer learning](https://www.tensorflow.org/tutorials/image_retraining) to shorten the amount of time needed to train the entire model. This means we can take an existing model (that has been trained well on a related domain (here image classification) and retrain its final layer(s) to detect hands for us. Sweet!. Given that neural networks sometimes have thousands or millions of parameters that can take weeks or months to train, transfer learning helps shorten training time to possibly hours. Tensorflow does offer a few models (in the tensorflow [model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md#coco-trained-models-coco-models)) and I chose to use the `ssd_mobilenet_v1_coco` model as my start point given it is currently (one of) the fastest models (read the SSD research [paper here](https://arxiv.org/pdf/1512.02325.pdf)). The training process can be done locally on your CPU machine which may take a while or better on a (cloud) GPU machine (which is what I did). For reference, training on my macbook pro (tensorflow compiled from source to take advantage of the mac's cpu architecture) the maximum speed I got was 5 seconds per step as opposed to the ~0.5 seconds per step I got with a GPU. For reference it would take about 12 days to run 200k steps on my mac (i7, 2.5GHz, 16GB) compared to ~5hrs on a GPU. > **Training on your own images**: Please use the [guide provided by Harrison from pythonprogramming](https://pythonprogramming.net/training-custom-objects-tensorflow-object-detection-api-tutorial/) on how to generate tfrecords given your label csv files and your images. The guide also covers how to start the training process if training locally. [see [here] (https://pythonprogramming.net/training-custom-objects-tensorflow-object-detection-api-tutorial/)]. If training in the cloud using a service like GCP, see the [guide here](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/running_on_cloud.md). As the training process progresses, the expectation is that total loss (errors) gets reduced to its possible minimum (about a value of 1 or thereabout). By observing the tensorboard graphs for total loss(see image below), it should be possible to get an idea of when the training process is complete (total loss does not decrease with further iterations/steps). I ran my training job for 200k steps (took about 5 hours) and stopped at a total Loss (errors) value of 2.575.(In retrospect, I could have stopped the training at about 50k steps and gotten a similar total loss value). With tensorflow, you can also run an evaluation concurrently that assesses your model to see how well it performs on the test data. A commonly used metric for performance is mean average precision (mAP) which is single number used to summarize the area under the precision-recall curve. mAP is a measure of how well the model generates a bounding box that has at least a 50% overlap with the ground truth bounding box in our test dataset. For the hand detector trained here, the mAP value was **0.9686@0.5IOU**. mAP values range from 0-1, the higher the better. <img src="images/accuracy.jpg" width="100%"> Once training is completed, the trained inference graph (`frozen_inference_graph.pb`) is then exported (see the earlier referenced guides for how to do this) and saved in the `hand_inference_graph` folder. Now its time to do some interesting detection. ## Using the Detector to Detect/Track hands If you have not done this yet, please following the guide on installing [Tensorflow and the Tensorflow object detection api](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md). This will walk you through setting up the tensorflow framework, cloning the tensorflow github repo and a guide on - Load the `frozen_inference_graph.pb` trained on the hands dataset as well as the corresponding label map. In this repo, this is done in the `utils/detector_utils.py` script by the `load_inference_graph` method. ```python detection_graph = tf.Graph() with detection_graph.as_default(): od_graph_def = tf.GraphDef() with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid: serialized_graph = fid.read() od_graph_def.ParseFromString(serialized_graph) tf.import_graph_def(od_graph_def, name='') sess = tf.Session(graph=detection_graph) print("> ====== Hand Inference graph loaded.") ``` - Detect hands. In this repo, this is done in the `utils/detector_utils.py` script by the `detect_objects` method. ```python (boxes, scores, classes, num) = sess.run( [detection_boxes, detection_scores, detection_classes, num_detections], feed_dict={image_tensor: image_np_expanded}) ``` - Visualize detected bounding detection_boxes. In this repo, this is done in the `utils/detector_utils.py` script by the `draw_box_on_image` method. This repo contains two scripts that tie all these steps together. - detect_multi_threaded.py : A threaded implementation for reading camera video input detection and detecting. Takes a set of command line flags to set parameters such as `--display` (visualize detections), image parameters `--width` and `--height`, videe `--source` (0 for camera) etc. - detect_single_threaded.py : Same as above, but single threaded. This script works for video files by setting the video source parameter videe `--source` (path to a video file). ```cmd # load and run detection on video at path "videos/chess.mov" python detect_single_threaded.py --source videos/chess.mov ``` > Update: If you do have errors loading the frozen inference graph in this repo, feel free to generate a new graph that fits your TF version from the model-checkpoint in this repo. Use the [export_inference_graph.py](https://github.com/tensorflow/models/blob/master/research/object_detection/export_inference_graph.py) script provided in the tensorflow object detection api repo. More guidance on this [here](https://pythonprogramming.net/testing-custom-object-detector-tensorflow-object-detection-api-tutorial/?completed=/training-custom-objects-tensorflow-object-detection-api-tutorial/). ## Thoughts on Optimization. A few things that led to noticeable performance increases. - Threading: Turns out that reading images from a webcam is a heavy I/O event and if run on the main application thread can slow down the program. I implemented some good ideas from [Adrian Rosebuck](https://www.pyimagesearch.com/2017/02/06/faster-video-file-fps-with-cv2-videocapture-and-opencv/) on parrallelizing image capture across multiple worker threads. This mostly led to an FPS increase of about 5 points. - For those new to Opencv, images from the `cv2.read()` method return images in [BGR format](https://www.learnopencv.com/why-does-opencv-use-bgr-color-format/). Ensure you convert to RGB before detection (accuracy will be much reduced if you dont). ```python cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB) ``` - Keeping your input image small will increase fps without any significant accuracy drop.(I used about 320 x 240 compared to the 1280 x 720 which my webcam provides). - Model Quantization. Moving from the current 32 bit to 8 bit can achieve up to 4x reduction in memory required to load and store models. One way to further speed up this model is to explore the use of [8-bit fixed point quantization](https://heartbeat.fritz.ai/8-bit-quantization-and-tensorflow-lite-speeding-up-mobile-inference-with-low-precision-a882dfcafbbd). Performance can also be increased by a clever combination of tracking algorithms with the already decent detection and this is something I am still experimenting with. Have ideas for optimizing better, please share! <img src="images/general.jpg" width="100%"> Note: The detector does reflect some limitations associated with the training set. This includes non-egocentric viewpoints, very noisy backgrounds (e.g in a sea of hands) and sometimes skin tone. There is opportunity to improve these with additional data. ## Integrating Multiple DNNs. One way to make things more interesting is to integrate our new knowledge of where "hands" are with other detectors trained to recognize other objects. Unfortunately, while our hand detector can in fact detect hands, it cannot detect other objects (a factor or how it is trained). To create a detector that classifies multiple different objects would mean a long involved process of assembling datasets for each class and a lengthy training process. > Given the above, a potential strategy is to explore structures that allow us **efficiently** interleave output form multiple pretrained models for various object classes and have them detect multiple objects on a single image. An example of this is with my primary use case where I am interested in understanding the position of objects on a table with respect to hands on same table. I am currently doing some work on a threaded application that loads multiple detectors and outputs bounding boxes on a single image. More on this soon.

This repository is based on AI and Machine learning for Coders book, I note my knowledge I have learned from the book in Vietnamese.

Notes and exploration code for learning about AI/ML

Recommendation Systems This is a workshop on using Machine Learning and Deep Learning Techniques to build Recommendation Systesm Theory: ML & DL Formulation, Prediction vs. Ranking, Similiarity, Biased vs. Unbiased Paradigms: Content-based, Collaborative filtering, Knowledge-based, Hybrid and Ensembles Data: Tabular, Images, Text (Sequences) Models: (Deep) Matrix Factorisation, Auto-Encoders, Wide & Deep, Rank-Learning, Sequence Modelling Methods: Explicit vs. implicit feedback, User-Item matrix, Embeddings, Convolution, Recurrent, Domain Signals: location, time, context, social, Process: Setup, Encode & Embed, Design, Train & Select, Serve & Scale, Measure, Test & Improve Tools: python-data-stack: numpy, pandas, scikit-learn, keras, spacy, implicit, lightfm Notes & Slides Basics: Deep Learning AI Conference 2019: WhiteBoard Notes | In-Class Notebooks Notebooks Movies - Movielens 01-Acquire 02-Augment 03-Refine 04-Transform 05-Evaluation 06-Model-Baseline 07-Feature-extractor 08-Model-Matrix-Factorization 09-Model-Matrix-Factorization-with-Bias 10-Model-MF-NNMF 11-Model-Deep-Matrix-Factorization 12-Model-Neural-Collaborative-Filtering 13-Model-Implicit-Matrix-Factorization 14-Features-Image 15-Features-NLP Ecommerce - YooChoose 01-Data-Preparation 02-Models News - Hackernews Product - Groceries Python Libraries Deep Recommender Libraries Tensorrec - Built on Tensorflow Spotlight - Built on PyTorch TFranking - Built on TensorFlow (Learning to Rank) Matrix Factorisation Based Libraries Implicit - Implicit Matrix Factorisation QMF - Implicit Matrix Factorisation Lightfm - For Hybrid Recommedations Surprise - Scikit-learn type api for traditional alogrithms Similarity Search Libraries Annoy - Approximate Nearest Neighbour NMSLib - kNN methods FAISS - Similarity search and clustering Learning Resources Reference Slides Deep Learning in RecSys by Balázs Hidasi Lessons from Industry RecSys by Xavier Amatriain Architecting Recommendation Systems by James Kirk Recommendation Systems Overview by Raimon and Basilico Benchmarks MovieLens Benchmarks for Traditional Setup Microsoft Tutorial on Recommendation System at KDD 2019 Algorithms & Approaches Collaborative Filtering for Implicit Feedback Datasets Bayesian Personalised Ranking for Implicit Data Logistic Matrix Factorisation Neural Network Matrix Factorisation Neural Collaborative Filtering Variational Autoencoders for Collaborative Filtering Evaluations Evaluating Recommendation Systems

Here are my personal paper reading notes (including machine learning systems, AI infrastructure, and other interesting stuffs).

This respository having all my notes of Machine learning specialization course by Deep learning.ai

Lecture notes, slides and scripts (LaTeX sources) in AI, Robotics, Machine Learning, Maths, Optimization

Just some stuff for Interview questions, books, annotated paper, notes, cheat sheets etc etc related to ML,AI, Deep Learning and Data Science

Programming assignments and lecture notes of the Deep Learning Specialization taught by Andrew Ng and offered by deeplearning.ai on Coursera.

🐍 A Collection of Notes for Learning & Understanding Deep Learning / Machine Learning / Artificial Intelligence (AI) with TensorFlow 🐍

What is C#? C# is pronounced "C-Sharp". It is an object-oriented programming language created by Microsoft that runs on the .NET Framework. C# has roots from the C family, and the language is close to other popular languages like C++ and Java. The first version was released in year 2002. The latest version, C# 8, was released in September 2019. C# is a modern object-oriented programming language developed in 2000 by Anders Hejlsberg, the principal designer and lead architect at Microsoft. It is pronounced as "C-Sharp," inspired by the musical notation “♯” which stands for a note with a slightly higher pitch. As it’s considered an incremental compilation of the C++ language, the name C “sharp” seemed most appropriate. The sharp symbol, however, has been replaced by the keyboard friendly “#” as a suffix to “C” for purposes of programming. Although the code is very similar to C++, C# is newer and has grown fast with extensive support from Microsoft. The fact that it’s so similar to Java syntactically helps explain why it has emerged as one of the most popular programming languages today. C# is pronounced "C-Sharp". It is an object-oriented programming language created by Microsoft that runs on the .NET Framework. C# has roots from the C family, and the language is close to other popular languages like C++ and Java. The first version was released in year 2002. The latest version, C# 8, was released in September 2019. C# is used for: Mobile applications Desktop applications Web applications Web services Web sites Games VR Database applications And much, much more! An Introduction to C# Programming C# is a general-purpose, object-oriented programming language that is structured and easy to learn. It runs on Microsoft’s .Net Framework and can be compiled on a variety of computer platforms. As the syntax is simple and easy to learn, developers familiar with C, C++, or Java have found a comfort zone within C#. C# is a boon for developers who want to build a wide range of applications on the .NET Framework—Windows applications, Web applications, and Web services—in addition to building mobile apps, Windows Store apps, and enterprise software. It is thus considered a powerful programming language and features in every developer’s cache of tools. Although first released in 2002, when it was introduced with .NET Framework 1.0, the C# language has evolved a great deal since then. The most recent version is C# 8.0, available in preview as part of Visual Studio. To get access to all of the new language features, you would need to install the latest preview version of .NET Core 3.0. C# is used for: Mobile applications Desktop applications Web applications Web services Web sites Games VR Database applications And much, much more! Why Use C#? It is one of the most popular programming language in the world It is easy to learn and simple to use It has a huge community support C# is an object oriented language which gives a clear structure to programs and allows code to be reused, lowering development costs. As C# is close to C, C++ and Java, it makes it easy for programmers to switch to C# or vice versa. The C# Environment You need the .NET Framework and an IDE (integrated development environment) to work with the C# language. The .NET Framework The .NET Framework platform of the Windows OS is required to write web and desktop-based applications using not only C# but also Visual Basic and Jscript, as the platform provides language interoperability. Besides, the .Net Framework allows C# to communicate with any of the other common languages, such as C++, Jscript, COBOL, and so on. IDEs Microsoft provides various IDEs for C# programming: Visual Studio 2010 (VS) Visual Studio Express Visual Web Developer Visual Studio Code (VSC) The C# source code files can be written using a basic text editor, like Notepad, and compiled using the command-line compiler of the .NET Framework. Alternative open-source versions of the .Net Framework can work on other operating systems as well. For instance, the Mono has a C# compiler and runs on several operating systems, including Linux, Mac, Android, BSD, iOS, Windows, Solaris, and UNIX. This brings enhanced development tools to the developer. As C# is part of the .Net Framework platform, it has access to its enormous library of codes and components, such as Common Language Runtime (CLR), the .Net Framework Class Library, Common Language Specification, Common Type System, Metadata and Assemblies, Windows Forms, ASP.Net and ASP.Net AJAX, Windows Workflow Foundation (WF), Windows Communication Foundation (WCF), and LINQ. C# and Java C# and Java are high-level programming languages that share several similarities (as well as many differences). They are both object-oriented languages much influenced by C++. But while C# is suitable for application development in the Microsoft ecosystem from the front, Java is considered best for client-side web applications. Also, while C# has many tools for programming, Java has a larger arsenal of tools to choose from in IDEs and Text Editors. C# is used for virtual reality projects like games, mobile, and web applications. It is built specifically for Microsoft platforms and several non-Microsoft-based operating systems, like the Mono Project that works with Linux and OS X. Java is used for creating messaging applications and developing web-based and enterprise-based applications in open-source ecosystems. Both C# and Java support arrays. However, each language uses them differently. In C#, arrays are a specialization of the system; in Java, they are a direct specialization of the object. The C# programming language executes on the CLR. The source code is interpreted into bytecode, which is further compiled by the CLR. Java runs on any platform with the assistance of JRE (Java Runtime Environment). The written source code is first compiled into bytecode and then converted into machine code to be executed on a JRE. C# and C++ Although C# and C++ are both C-based languages with similar code, there are some differences. For one, C# is considered a component-oriented programming language, while C++ is a partial object-oriented language. Also, while both languages are compiled languages, C# compiles to CLR and is interpreted by.NET, but C++ compiles to machine code. The size of binaries in C# is much larger than in C++. Other differences between the two include the following: C# gives compiler errors and warnings, but C++ doesn’t support warnings, which may cause damage to the OS. C# runs in a virtual machine for automatic memory management. C++ requires you to manage memory manually. C# can create Windows, .NET, web, desktop, and mobile applications, but not stand-alone apps. C++ can create server-side, stand-alone, and console applications as it can work directly with the hardware. C++ can be used on any platform, while C# is targeted toward Windows OS. Generally, C++ being faster than C#, the former is preferred for applications where performance is essential. Features of C# The C# programming language has many features that make it more useful and unique when compared to other languages, including: Object-oriented language Being object-oriented, C# allows the creation of modular applications and reusable codes, an advantage over C++. As an object-oriented language, C# makes development and maintenance easier when project size grows. It supports all three object-oriented features: data encapsulation, inheritance, interfaces, and polymorphism. Simplicity C# is a simple language with a structured approach to problem-solving. Unsafe operations, like direct memory manipulation, are not allowed. Speed The compilation and execution time in C# is very powerful and fast. A Modern programming language C# programming is used for building scalable and interoperable applications with support for modern features like automatic garbage collection, error handling, debugging, and robust security. It has built-in support for a web service to be invoked from any app running on any platform. Type-safe Arrays and objects are zero base indexed and bound checked. There is an automatic checking of the overflow of types. The C# type safety instances support robust programming. Interoperability Language interoperability of C# maximizes code reuse for the efficiency of the development process. C# programs can work upon almost anything as a program can call out any native API. Consistency Its unified type system enables developers to extend the type system simply and easily for consistent behavior. Updateable C# is automatically updateable. Its versioning support enables complex frameworks to be developed and evolved. Component oriented C# supports component-oriented programming through the concepts of properties, methods, events, and attributes for self-contained and self-describing components of functionality for robust and scalable applications. Structured Programming Language The structured design and modularization in C# break a problem into parts, using functions for easy implementation to solve significant problems. Rich Library C# has a standard library with many inbuilt functions for easy and fast development. Prerequisites for Learning C# Basic knowledge of C or C++ or any programming language or programming fundamentals. Additionally, the OOP concept makes for a short learning curve of C#. Advantages of C# There are many advantages to the C# language that makes it a useful programming language compared to other languages like Java, C, or C++. These include: Being an object-oriented language, C# allows you to create modular, maintainable applications and reusable codes Familiar syntax Easy to develop as it has a rich class of libraries for smooth implementation of functions Enhanced integration as an application written in .NET will integrate and interpret better when compared to other NET technologies As C# runs on CLR, it makes it easy to integrate with components written in other languages It’s safe, with no data loss as there is no type-conversion so that you can write secure codes The automatic garbage collection keeps the system clean and doesn’t hang it during execution As your machine has to install the .NET Framework to run C#, it supports cross-platform Strong memory backup prevents memory leakage Programming support of the Microsoft ecosystem makes development easy and seamless Low maintenance cost, as C# can develop iOS, Android, and Windows Phone native apps The syntax is similar to C, C++, and Java, which makes it easier to learn and work with C# Useful as it can develop iOS, Android, and Windows Phone native apps with the Xamarin Framework C# is the most powerful programming language for the .NET Framework Fast development as C# is open source steered by Microsoft with access to open source projects and tools on Github, and many active communities contributing to the improvement What Can C Sharp Do for You? C# can be used to develop a wide range of: Windows client applications Windows libraries and components Windows services Web applications Native iOS and Android mobile apps Azure cloud applications and services Gaming consoles and gaming systems Video and virtual reality games Interoperability software like SharePoint Enterprise software Backend services and database programs AI and ML applications Distributed applications Hardware-level programming Virus and malware software GUI-based applications IoT devices Blockchain and distributed ledger technology C# Programming for Beginners: Introduction, Features and Applications By Simplilearn Last updated on Jan 20, 2020674 C# Programming for Beginners As a programmer, you’re motivated to master the most popular languages that will give you an edge in your career. There’s a vast number of programming languages that you can learn, but how do you know which is the most useful? If you know C and C++, do you need to learn C# as well? How similar is C# to Java? Does it become more comfortable for you to learn C# if you already know Java? Every developer and wannabe programmer asks these types of questions. So let us explore C# programming: how it evolved as an extension of C and why you need to learn it as a part of the Master’s Program in integrated DevOps for server-side execution. Are you a web developer or someone interested to build a website? Enroll for the Javascript Certification Training. Check out the course preview now! What is C#? C# is a modern object-oriented programming language developed in 2000 by Anders Hejlsberg, the principal designer and lead architect at Microsoft. It is pronounced as "C-Sharp," inspired by the musical notation “♯” which stands for a note with a slightly higher pitch. As it’s considered an incremental compilation of the C++ language, the name C “sharp” seemed most appropriate. The sharp symbol, however, has been replaced by the keyboard friendly “#” as a suffix to “C” for purposes of programming. Although the code is very similar to C++, C# is newer and has grown fast with extensive support from Microsoft. The fact that it’s so similar to Java syntactically helps explain why it has emerged as one of the most popular programming languages today. An Introduction to C# Programming C# is a general-purpose, object-oriented programming language that is structured and easy to learn. It runs on Microsoft’s .Net Framework and can be compiled on a variety of computer platforms. As the syntax is simple and easy to learn, developers familiar with C, C++, or Java have found a comfort zone within C#. C# is a boon for developers who want to build a wide range of applications on the .NET Framework—Windows applications, Web applications, and Web services—in addition to building mobile apps, Windows Store apps, and enterprise software. It is thus considered a powerful programming language and features in every developer’s cache of tools. Although first released in 2002, when it was introduced with .NET Framework 1.0, the C# language has evolved a great deal since then. The most recent version is C# 8.0, available in preview as part of Visual Studio. To get access to all of the new language features, you would need to install the latest preview version of .NET Core 3.0. The C# Environment You need the .NET Framework and an IDE (integrated development environment) to work with the C# language. The .NET Framework The .NET Framework platform of the Windows OS is required to write web and desktop-based applications using not only C# but also Visual Basic and Jscript, as the platform provides language interoperability. Besides, the .Net Framework allows C# to communicate with any of the other common languages, such as C++, Jscript, COBOL, and so on. IDEs Microsoft provides various IDEs for C# programming: Visual Studio 2010 (VS) Visual Studio Express Visual Web Developer Visual Studio Code (VSC) The C# source code files can be written using a basic text editor, like Notepad, and compiled using the command-line compiler of the .NET Framework. Alternative open-source versions of the .Net Framework can work on other operating systems as well. For instance, the Mono has a C# compiler and runs on several operating systems, including Linux, Mac, Android, BSD, iOS, Windows, Solaris, and UNIX. This brings enhanced development tools to the developer. As C# is part of the .Net Framework platform, it has access to its enormous library of codes and components, such as Common Language Runtime (CLR), the .Net Framework Class Library, Common Language Specification, Common Type System, Metadata and Assemblies, Windows Forms, ASP.Net and ASP.Net AJAX, Windows Workflow Foundation (WF), Windows Communication Foundation (WCF), and LINQ. C# and Java C# and Java are high-level programming languages that share several similarities (as well as many differences). They are both object-oriented languages much influenced by C++. But while C# is suitable for application development in the Microsoft ecosystem from the front, Java is considered best for client-side web applications. Also, while C# has many tools for programming, Java has a larger arsenal of tools to choose from in IDEs and Text Editors. C# is used for virtual reality projects like games, mobile, and web applications. It is built specifically for Microsoft platforms and several non-Microsoft-based operating systems, like the Mono Project that works with Linux and OS X. Java is used for creating messaging applications and developing web-based and enterprise-based applications in open-source ecosystems. Both C# and Java support arrays. However, each language uses them differently. In C#, arrays are a specialization of the system; in Java, they are a direct specialization of the object. The C# programming language executes on the CLR. The source code is interpreted into bytecode, which is further compiled by the CLR. Java runs on any platform with the assistance of JRE (Java Runtime Environment). The written source code is first compiled into bytecode and then converted into machine code to be executed on a JRE. C# and C++ Although C# and C++ are both C-based languages with similar code, there are some differences. For one, C# is considered a component-oriented programming language, while C++ is a partial object-oriented language. Also, while both languages are compiled languages, C# compiles to CLR and is interpreted by.NET, but C++ compiles to machine code. The size of binaries in C# is much larger than in C++. Other differences between the two include the following: C# gives compiler errors and warnings, but C++ doesn’t support warnings, which may cause damage to the OS. C# runs in a virtual machine for automatic memory management. C++ requires you to manage memory manually. C# can create Windows, .NET, web, desktop, and mobile applications, but not stand-alone apps. C++ can create server-side, stand-alone, and console applications as it can work directly with the hardware. C++ can be used on any platform, while C# is targeted toward Windows OS. Generally, C++ being faster than C#, the former is preferred for applications where performance is essential. Features of C# The C# programming language has many features that make it more useful and unique when compared to other languages, including: Object-oriented language Being object-oriented, C# allows the creation of modular applications and reusable codes, an advantage over C++. As an object-oriented language, C# makes development and maintenance easier when project size grows. It supports all three object-oriented features: data encapsulation, inheritance, interfaces, and polymorphism. Simplicity C# is a simple language with a structured approach to problem-solving. Unsafe operations, like direct memory manipulation, are not allowed. Speed The compilation and execution time in C# is very powerful and fast. A Modern programming language C# programming is used for building scalable and interoperable applications with support for modern features like automatic garbage collection, error handling, debugging, and robust security. It has built-in support for a web service to be invoked from any app running on any platform. Type-safe Arrays and objects are zero base indexed and bound checked. There is an automatic checking of the overflow of types. The C# type safety instances support robust programming. Interoperability Language interoperability of C# maximizes code reuse for the efficiency of the development process. C# programs can work upon almost anything as a program can call out any native API. Consistency Its unified type system enables developers to extend the type system simply and easily for consistent behavior. Updateable C# is automatically updateable. Its versioning support enables complex frameworks to be developed and evolved. Component oriented C# supports component-oriented programming through the concepts of properties, methods, events, and attributes for self-contained and self-describing components of functionality for robust and scalable applications. Structured Programming Language The structured design and modularization in C# break a problem into parts, using functions for easy implementation to solve significant problems. Rich Library C# has a standard library with many inbuilt functions for easy and fast development. Full Stack Java Developer Course The Gateway to Master Web DevelopmentEXPLORE COURSEFull Stack Java Developer Course Prerequisites for Learning C# Basic knowledge of C or C++ or any programming language or programming fundamentals. Additionally, the OOP concept makes for a short learning curve of C#. Advantages of C# There are many advantages to the C# language that makes it a useful programming language compared to other languages like Java, C, or C++. These include: Being an object-oriented language, C# allows you to create modular, maintainable applications and reusable codes Familiar syntax Easy to develop as it has a rich class of libraries for smooth implementation of functions Enhanced integration as an application written in .NET will integrate and interpret better when compared to other NET technologies As C# runs on CLR, it makes it easy to integrate with components written in other languages It’s safe, with no data loss as there is no type-conversion so that you can write secure codes The automatic garbage collection keeps the system clean and doesn’t hang it during execution As your machine has to install the .NET Framework to run C#, it supports cross-platform Strong memory backup prevents memory leakage Programming support of the Microsoft ecosystem makes development easy and seamless Low maintenance cost, as C# can develop iOS, Android, and Windows Phone native apps The syntax is similar to C, C++, and Java, which makes it easier to learn and work with C# Useful as it can develop iOS, Android, and Windows Phone native apps with the Xamarin Framework C# is the most powerful programming language for the .NET Framework Fast development as C# is open source steered by Microsoft with access to open source projects and tools on Github, and many active communities contributing to the improvement What Can C Sharp Do for You? C# can be used to develop a wide range of: Windows client applications Windows libraries and components Windows services Web applications Native iOS and Android mobile apps Azure cloud applications and services Gaming consoles and gaming systems Video and virtual reality games Interoperability software like SharePoint Enterprise software Backend services and database programs AI and ML applications Distributed applications Hardware-level programming Virus and malware software GUI-based applications IoT devices Blockchain and distributed ledger technology Who Should Learn the C# Programming Language and Why? C# is one of the most popular programming languages as it can be used for a variety of applications: mobile apps, game development, and enterprise software. What’s more, the C# 8.0 version is packed with several new features and enhancements to the C# language that can change the way developers write their C# code. The most important new features available are ‘null reference types,’ enhanced ‘pattern matching,’ and ‘async streams’ that help you to write more reliable and readable code. As you’re exposed to the fundamental programming concepts of C# in this course, you can work on projects that open the doors for you as a Full Stack Java Developer. So, upskill and master the C# language for a faster career trajectory and salary scope.

Effortlessly convert YouTube lectures to concise notes with our AI transcriber. Streamline learning and comprehension with just a click!

Notes and codes of my self-learning courses, including AI / Web3 / Computer Science

Contains all course modules, exercises and notes of Deep Learning Specialization by Andrew Ng, and DeepLearning.ai in Coursera

医学AI-学习笔记 (Medical AI - Learning Notes)

Convert lecture videos to notes using AI & machine learning. Code for the research titled "Lecture2Notes: Summarizing Lecture Videos by Classifying Slides and Analyzing Text using Machine Learning."

A comprehensive learning path and practical guide for Generative AI development with hands-on LangChain implementations and detailed notes.

Inspired by Ilya Sutskever’s 2020 reading list to John Carmack, this repo reproduces and explores key AI papers, known as the "ilya30u30." Dive into detailed notes, code, and insights to deepen your understanding of foundational and advanced deep learning concepts.

Applied AI(https://www.appliedaicourse.com/course/11/Applied-Machine-learning-course) Notes Prepared by me.

A curated collection of books, summaries, and resources focused on artificial intelligence and machine learning, covering algorithms, deep learning, and practical applications. Designed to support learners and professionals in mastering AI/ML concepts with recommended readings and notes.

Random AI notes for working with local models or playing around with random machine learning bits.

These are my learning notes about robot learning and Embodied AI[具身智能学习笔记]. If you feel it hard to learn them, please star me!

Research Paper Summaries, Setup & Performance Notes, Resource Links on AI, Deep Learning, NLP, Computer Vision for my learning.

Educational platform utilising Google AI to enhance student learning and note-taking experience. The app will offer features like voice note recording, transcription, image-to-text conversion, text-to-PDF generation, ChatGPT integration for summarisation and translation, a user-friendly interface, cloud storage, and customisation options.