A roadmap for "generative AI" learning resources

https://dongzl.github.io/netty-handbook/

The Haskell Optimization Handbook

TKE指南：https://tencentcloudcontainerteam.github.io/tke-handbook/

This site is all for DevOps Handbook, I am translator of this book.

AI Practitioner Handbook | https://aisingapore.github.io/ai-practitioner-handbook/

Empowering VA teams to design and build great digital services on the Veteran-facing Services Platform --- https://department-of-veterans-affairs.github.io/va-digital-service-handbook/

how to test microservices based applications? Moved to: https://github.com/stakater/developer-handbook

Discipline for understanding rack - from https://github.com/miyagawa/plack-handbook

trunk based development continuous delivery workflow; Moved to: https://github.com/stakater/developer-handbook

See at http://pamelafox.github.io/developer-support-handbook/

A complete Git and GitHub guide for Termux on Android, covering setup, repository management, collaboration, recovery, and essential commands.

It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. Content The dataset contains transactions made by credit cards in September 2013 by European cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions. It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, … V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise. Given the class imbalance ratio, we recommend measuring the accuracy using the Area Under the Precision-Recall Curve (AUPRC). Confusion matrix accuracy is not meaningful for unbalanced classification. Update (03/05/2021) A simulator for transaction data has been released as part of the practical handbook on Machine Learning for Credit Card Fraud Detection - https://fraud-detection-handbook.github.io/fraud-detection-handbook/Chapter_3_GettingStarted/SimulatedDataset.html. We invite all practitioners interested in fraud detection datasets to also check out this data simulator, and the methodologies for credit card fraud detection presented in the book. Acknowledgements The dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available on https://www.researchgate.net/project/Fraud-detection-5 and the page of the DefeatFraud project Please cite the following works: Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015 Dal Pozzolo, Andrea; Caelen, Olivier; Le Borgne, Yann-Ael; Waterschoot, Serge; Bontempi, Gianluca. Learned lessons in credit card fraud detection from a practitioner perspective, Expert systems with applications,41,10,4915-4928,2014, Pergamon Dal Pozzolo, Andrea; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca. Credit card fraud detection: a realistic modeling and a novel learning strategy, IEEE transactions on neural networks and learning systems,29,8,3784-3797,2018,IEEE Dal Pozzolo, Andrea Adaptive Machine learning for credit card fraud detection ULB MLG PhD thesis (supervised by G. Bontempi) Carcillo, Fabrizio; Dal Pozzolo, Andrea; Le Borgne, Yann-Aël; Caelen, Olivier; Mazzer, Yannis; Bontempi, Gianluca. Scarff: a scalable framework for streaming credit card fraud detection with Spark, Information fusion,41, 182-194,2018,Elsevier Carcillo, Fabrizio; Le Borgne, Yann-Aël; Caelen, Olivier; Bontempi, Gianluca. Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization, International Journal of Data Science and Analytics, 5,4,285-300,2018,Springer International Publishing Bertrand Lebichot, Yann-Aël Le Borgne, Liyun He, Frederic Oblé, Gianluca Bontempi Deep-Learning Domain Adaptation Techniques for Credit Cards Fraud Detection, INNSBDDL 2019: Recent Advances in Big Data and Deep Learning, pp 78-88, 2019 Fabrizio Carcillo, Yann-Aël Le Borgne, Olivier Caelen, Frederic Oblé, Gianluca Bontempi Combining Unsupervised and Supervised Learning in Credit Card Fraud Detection Information Sciences, 2019 Yann-Aël Le Borgne, Gianluca Bontempi Machine Learning for Credit Card Fraud Detection - Practical Handbook

Notes made while reading Python Data Science Handbook by Jake VanderPlas (https://github.com/jakevdp/PythonDataScienceHandbook)

A handbook for mathematicians who want to get productive using GitHub

Handbook for Octokit maintainers and GitHub integrators

My Jupyter notebooks based on the book Python Data Science Handbook (https://jakevdp.github.io/PythonDataScienceHandbook)

A community-curated collection of Minecraft map-making tricks and tips. https://minecraftcommands.github.io/commanders-handbook

Welcome to the "System Administration & Security Handbook" GitHub repository! This handbook is a comprehensive guide derived from my CS 410 System Admin & Security final project, taught by Dan Carrere at the University of Oregon. It serves as a starting point for aspiring system administrators and security experts.

📚 Data Science Experiment Handbook: 一个致力于深入探索和分享如何识别GitHub异常账户的数据科学实验手册。本仓库包括从数据获取、预处理、特征工程到模型构建与评估的完整流程。欢迎各位数据科学爱好者、研究者和开发者共同参与与贡献!

This library is a part of Telink Sig Mesh SDK. Published to GitHub for personal usage only. Visit the Telink Wiki to acquire the full SDK zip file and development handbooks.

The FRAPI github page - Used for the FRAPI developers handbook

A collection of handbooks for the Capture the Flag game by rubenwardy on Luanti

The Scientific Computing Handbook: Your definitive guide to implementing algorithms for scientific computing in C++, Python, and Julia

👨‍💻 Developer handbook, It contains all info that a developer used in daily life (Git commands, GitHub Action, HTTP response status etc...)

https://moja-global.github.io/Handbook/

Handbook on how to install and use pando, as well as reproduce performance experiments performed for published papers. https://elavoie.github.io/pando-handbook/

A platform engineering handbook covering Kubernetes, OpenShift, Argo CD, Flux CD, AWS, Azure, Terraform, and GitHub Actions — with an optional Claude plugin layer for interactive guidance.

Tips about how to make your GitHub projects awesome!

Articles about debugging - now published from the handbook https://github.com/ARMmbed/Handbook/