Popular GitHub Repositories for Nn

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》)

Ruby library for interfacing with FANN (Fast Artificial Neural Network)

Neural Network in Rust

A Keras CTC implementation of Baidu's DeepSpeech for model experimentation

Generating Cat images using StyleGAN

A Deep Learning and preprocessing framework in Rust with support for CPU and GPU.

dcgan combined with vae in pytorch!

To address some of the failure modes in training of physics informed neural networks, a Lagrangian architecture is designed to conform to the direction of travel of information in convection-diffusion equations, i.e., method of characteristic; The repository includes a pytorch implementation of PINN and proposed LPINN with periodic boundary conditions

Cheat Sheets for deep learning and machine learning.

This is a self-played snake game that uses genetic algorithm and neural network to learn and improve its gameplay over time. The game is built using the p5.js library for graphics and user interface, and the tensorflow.js and neataptic libraries for machine learning.

Нейронная сеть оптимизируемая с помощью генетического алгоритма. Задача агента контролируемого при помощи нейронной сети состоит в том, чтобы избегать контакта с противниками, как можно более длительное время.

LSTM Network Implementation for Linkin Park Lyrics ( Generation + Prediction )

Machine learning library for creating and working with GAN, RNN, CNN, R-CNN and also with Classic Neural Networks.

Optimization of Artificial Neural Networks with Genetic Algorithm

Training a simple perceptron network with one layer and multi input and multi output neurons

Artificial Neural Network(ANN)? Classification models developed using the PyTorch and Scikit-Learn libraries of python?!

A simple in-training visualization tool for neural networks built on pytorch models

This repository contains an experimental implementation of the Titans Transformer architecture for sequence modeling tasks. The code is a personal exploration and may include errors or inefficiencies as I am currently in the learning stage. It is inspired by the ideas presented in the original

AI learns to balance a pole hinged on a cart.

Inspired by Andrej Karpathy’s micrograd, this lecture builds a neural network from scratch, manually deriving gradients, automating backpropagation, and leveraging the TANH activation for nonlinearity. We bridge to PyTorch, demonstrating gradient descent’s power to minimize loss and reveal neural network fundamentals.