from vibe coding to agentic engineering - practice makes claude perfect
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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, ... 🧠
- deep-learning
- deep-learning-tutorial
- pytorch
- gan
PyTorch Tutorial for Deep Learning Researchers
- deep-learning
- pytorch-tutorial
- neural-networks
- pytorch
End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment.
💻 Vibe Coding 101｜Your First Course For AI Product Builders.
- ai
- course
- vibe-coding
- coding
Architecture decision record (ADR) examples for software planning, IT leadership, and template documentation
- adr
- documentation
- teamwork
- architecture-decision-record
Translated7 days golang programs from scratch (web framework Gee, distributed cache GeeCache, object relational mapping ORM framework GeeORM, rpc framework GeeRPC etc) A series on writing/implementing things from scratch in 7 days with Go
- learning
- starter-project
- golang
- scratch
What happens behind the scenes when we type www.google.com in a browser?
A neural network that transforms a design mock-up into a static website.
- keras
- deep-learning
- seq2seq
- encoder-decoder
TranslatedMachine learning tutorials
Pocket Flow: Codebase to Tutorial
FFmpeg libav tutorial - learn how media works from basic to transmuxing, transcoding and more. Translations: 🇺🇸 🇨🇳 🇰🇷 🇪🇸 🇻🇳 🇧🇷 🇷🇺
Tutorials, assignments, and competitions for MIT Deep Learning related courses.
- deep-learning
- mit
- self-driving-cars
- deep-reinforcement-learning
TensorFlow Tutorials with YouTube Videos
TensorFlow tutorials and best practices.
Go DDD example application. Complete project to show how to apply DDD, Clean Architecture, and CQRS by practical refactoring.
- go
- golang
- ddd
- clean-architecture
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.
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!
:earth_americas: Simple and ready-to-use tutorials for TensorFlow
Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier
- machine-learning
- deep-learning
- lstm
- human-activity-recognition
TensorFlow Tutorials
- tensorflow-tutorials
- convolutional-neural-networks
- recurrent-neural-networks
Implementing a Neural Network from Scratch