🐙 Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents.
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🚀✨ Help beginners to contribute to open source projects
- open-source
- tutorial
- contribution
- beginner
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
- tensorflow
- tutorial
- examples
- deep-learning
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.
50+ tutorials and implementations for Generative AI Agent techniques, from basic conversational bots to complex multi-agent systems.
TranslatedProgrammer Yupi's (程序员鱼皮) complete AI resource collection + zero-basics Vibe Coding tutorial. Shares step-by-step OpenClaw tutorials, ways to use large models (DeepSeek / GPT / Gemini / Claude / GLM), the latest AI news, a complete collection of Prompts, an AI knowledge encyclopedia (Agent Skills / RAG / MCP / A2A), AI programming tutorials (Harness Engineering), guides to using AI tools (Cursor / Claude Code / TRAE / Codex / Copilot), AI development framework tutorials (Spring AI / LangChain), and a guide to monetizing AI products, helping you quickly master AI technology and stay at the forefront of the times. This project is the open-source documentation aiguide, which has been upgraded to the Yupi AI Navigation website (鱼皮 AI 导航网站)
End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment.
🎮 A step-by-step guide to implementing SSAO, depth of field, lighting, normal mapping, and more for your 3D game.
A neural network that transforms a design mock-up into a static website.
- keras
- deep-learning
- seq2seq
- encoder-decoder
PyTorch tutorials and fun projects including neural talk, neural style, poem writing, anime generation (《深度学习框架PyTorch：入门与实战》)
- pytorch
- pytorch-tutorials
- pytorch-tutorials-cn
- deep-learning
:dolphin: A comprehensive tutorial on getting started with Docker!
- docker
- tutorials
- cloud-computing
- microservices
In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.
An absolute beginner's guide to Machine Learning and Image Classification with Neural Networks
Claude Code Guide - Setup, Commands, workflows, agents, skills & tips-n-tricks from beginner to power user!
The OWASP NodeGoat project provides an environment to learn how OWASP Top 10 security risks apply to web applications developed using Node.js and how to effectively address them.
full guide to help you integrate AI in your workflow, for both beginners and experienced users
Bringing thousands of learners together to unpack DeepInfer through peer-reviewed explanations, documentation, and hands-on examples.