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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, ... 🧠
A visual, example-driven guide to Claude Code — from basic concepts to advanced agents, with copy-paste templates that bring immediate value.
Getting Started with Spring Boot 3:
Welcome to the Llama Cookbook! This is your go to guide for Building with Llama: Getting started with Inference, Fine-Tuning, RAG. We also show you how to solve end to end problems using Llama model family and using them on various provider services
Microservice Architecture with Spring Boot, Spring Cloud and Docker
Android Transition animations explanation with examples.
The complete guide to static typing in "React & Redux" apps using TypeScript
Community guide to using YubiKey for GnuPG and SSH - protect secrets with hardware crypto.
Crack WPA/WPA2 Wi-Fi Routers with Airodump-ng and Aircrack-ng/Hashcat
TensorFlow Tutorials with YouTube Videos
Toturials coming with the "data science roadmap" picture.
:dolphin: A comprehensive tutorial on getting started with Docker!
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
how to build modular applications with browserify
Full Stack Web Development with Flask.
One stop shop for getting started with the Vulkan API
Handout for the tutorial "Creating publication-quality figures with matplotlib"
A 100-day challenge exploring IoT and embedded systems using ESP32, ESP8266, and Raspberry Pi Pico with MicroPython. Each day covers a new sensor or module with complete code, circuit diagram, and explanation.
Signal forecasting with a Sequence-to-Sequence (seq2seq) Recurrent Neural Network (RNN) model in TensorFlow - Guillaume Chevalier
Tinder like cards swipe effect with Flutter.
A Tutorial for Setting Python Development Environment with VScode and Docker
The ESP32-S3 bench guide as a React app: live pinout, lessons with interactive simulators, a code reader for the esp-labs firmware, and the bench log. In 11 languages.