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🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
📜 33 JavaScript concepts every developer should know.
🧠 Train a 64M-parameter LLM from scratch in just 2h!
100 Days of ML Coding
A visual, example-driven guide to Claude Code — from basic concepts to advanced agents, with copy-paste templates that bring immediate value.
An evolving how-to guide for securing a Linux server.
A playbook for systematically maximizing the performance of deep learning models.
50+ tutorials and implementations for Generative AI Agent techniques, from basic conversational bots to complex multi-agent systems.
Community guide to securing and improving privacy on macOS.
🛠️⚡ Step-by-step tutorial to build a modern JavaScript stack.
🎮 A step-by-step guide to implementing SSAO, depth of field, lighting, normal mapping, and more for your 3D game.
:mortar_board: All things Vim!
a collection of simple demos of React.js
A hands-on introduction to video technology: image, video, codec (av1, vp9, h265) and more (ffmpeg encoding). Translations: 🇺🇸 🇨🇳 🇯🇵 🇮🇹 🇰🇷 🇷🇺 🇧🇷 🇪🇸
:satellite: Simple and ready-to-use tutorials for TensorFlow
llama3 implementation one matrix multiplication at a time
TranslatedChinese tutorial on reinforcement learning (Mushroom Book 🍄), read online at: https://datawhalechina.github.io/easy-rl/
Android Transition animations explanation with examples.
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》)
This guide details creating a secure Linux production system. OpenSCAP (C2S/CIS, STIG).
Toturials coming with the "data science roadmap" picture.
Below are some simple methods for exiting vim.
Simple tutorials using Google's TensorFlow Framework
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.