47,133 repositories indexed
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Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.
Interactive Image Generation via Generative Adversarial Networks
Official tensorflow implementation for CVPR2020 paper “Learning to Cartoonize Using White-box Cartoon Representations”
FinRL-X: An AI-Native Modular Infrastructure for Quantitative Trading
A tiny list limited to the best CSS Learning Resources
Matlab/Octave toolbox for deep learning. Includes Deep Belief Nets, Stacked Autoencoders, Convolutional Neural Nets, Convolutional Autoencoders and vanilla Neural Nets. Each method has examples to get you started.
Lightweight library to build and train neural networks in Theano
TensorFlowOnSpark brings TensorFlow programs to Apache Spark clusters.
Fast Python Collaborative Filtering for Implicit Feedback Datasets
A python library for self-supervised learning and latent world models on images.
Java dataframe and visualization library
AI Infra / AI Orchestration / AI Control Plane
Code accompanying the book "Machine Learning for Hackers"
Bringing stable diffusion models to web browsers. Everything runs inside the browser with no server support.
A platform for Reasoning systems (Reinforcement Learning, Contextual Bandits, etc.)
OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.
A best practice for tensorflow project template architecture.
A curated collection of game development learning resources
The roadmap for learning the C++ programming language for beginners and experienced devs.
Bridge between QQ (SnowLuma OneBot v11) and DeepSeek Harness agents: social simulation, safe MCP tools, slang learning and more.
A standard API for multi-agent reinforcement learning environments, with popular reference environments and related utilities
🛠 All-in-one web-based IDE specialized for machine learning and data science.
Fast, flexible and easy to use probabilistic modelling in Python.
Seamlessly integrate LLMs into scikit-learn.