A Java implementation of Twitter's text processing library.
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Never face writer's block again. AI that transforms your thoughts into viral content for Twitter/X, LinkedIn, and Threads. Start your free trial.
Agent skill and MCP server for tweet search, user lookup, follower export, media download, webhooks, and confirmation-gated X actions. MIT.
Code samples for my book "Neural Networks and Deep Learning" [DEEP LEARNING].
A free video series by Andrej Karpathy covering neural networks from scratch — backpropagation, makemore, GPT, and more.
A general-purpose network embedding framework: pair-wise representations optimization Network Edit.
Generating retro pixel game characters with Generative Adversarial Networks. Dataset "TinyHero" included.
Animations and instructional videos about neural networks.
Platform for Neural Network Search (NAS) that allows you to generate efficient deep networks for your applications.
Fast Neural Networks framework built on top of Metal. Supports TensorFlow models.
Browser-based visual editor for designing neural networks and automatically generating PyTorch and TensorFlow code.
Neural networks in JavaScript - continued community fork of Brain.
The iOS and OS X neural network framework.
Fast, flexible and fun neural networks. This is the successor of PyBrain.
Theano based library for deep and recurrent neural networks.
Layer - Neural network inference from the command line, implemented in.
Project that automatically maps relationship networks by parsing public data using LLMs and visualizes it as an interactive graph.
Blog for understanding Neural Networks!
Neural networks, regression and feature learning in Clojure.
FANN (Fast Artificial Neural Network) binding.
Course materials and notes for Stanford class CS231n: Deep Learning for Computer Vision.
Torch CUDA Neural Network Implementation.
Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation.
Very simple implementation of neural networks for dummies in python without using any libraries, with detailed comments.