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:octocat: git and git flow cheat sheet
A list of web application security
edition 2 of the slambook
Slam Dunk JavaScript
Awesome paper list with code about generative adversarial nets
Go DDD example application. Complete project to show how to apply DDD, Clean Architecture, and CQRS by practical refactoring.
This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc
TranslatedAI algorithm job-hunting guide (covering preparation guides, problem-solving practice guides, internal referrals, a list of AI companies, and other materials)
TranslatedSimplified Chinese translation of the http://learnopengl.com tutorial series
TranslatedCommon English vocabulary in programmers' work
😎🏖️🐬 Your new, 𝙧𝙞𝙙𝙤𝙣𝙠𝙪𝙡𝙞𝙘𝙞𝙤𝙪𝙨𝙡𝙮 smart clipboard manager
📗📒 (PT-Br translation) JS Book Series.
Learn where some of the network sysctl variables fit into the Linux/Kernel network flow. Translations: 🇷🇺
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Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.
You don't need an animation library to add a simple effect to your SwiftUI app. Create it yourself with SwiftUI. This repo inspires you to add helpful and expressive SwiftUI animations like loading/progress, looping, on-off, enter, exit, fade, spin, and background animations to your next project. The repo also contains tremendous spring animations.
A collection of AWESOME things about domain adaptation
TranslatedMicroservices from Design to Deployment, Chinese version: 《微服务:从设计到部署》 (Microservices: From Design to Deployment)
Release repo for our SLAM Handbook
TranslatedChinese translation notes for Python for Data Analysis, 2nd Edition (2017)
TranslatedPython Best Practices Guide. The chinese translation of "Hitchhiker's Guide to Python".
TranslatedSearch all Chinese NLP datasets, with commonly used English NLP datasets included
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