TensorFlowtensorflow/tensorflow
Open source software library for numerical computation using data flow graphs.
443 libraries, 199 updated this year
Python holds well over a third of the list, and almost every major framework has its primary interface here. PyTorch, TensorFlow, JAX and Keras for deep learning; scikit-learn, XGBoost and LightGBM for classical models; Hugging Face Transformers for pretrained models; and NumPy, pandas and matplotlib underneath all of them.
443 libraries
Open source software library for numerical computation using data flow graphs.
A deep learning library containing thousands of pre-trained models on different tasks. The goto place for anything related to Large Language Models.
Tensors and Dynamic neural networks in Python with strong GPU acceleration
A data exploration platform designed to be visual, intuitive, and interactive.
A Python module for machine learning built on top of SciPy.
High-level neural networks frontend for TensorFlow, CNTK and Theano.
Face recognition library that recognizes and manipulates faces from Python or from the command line.
A library providing high-performance, easy-to-use data structures and data analysis tools.
Streamlit is an framework to create beautiful data apps in hours, not weeks.
RLlib is an industry level, highly scalable RL library for tf and torch, based on Ray. It's used by companies like Amazon and Microsoft to solve real-world decision making problems at scale.
A Python library for quickly creating and sharing demos of models. Debug models interactively in your browser, get feedback from collaborators, and generate public links without deploying anything.
An open-source deep learning system for large-scale model training and inference with high efficiency and low cost.
Open Source framework to streamline use of neural networks.
PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more.
JAX is Autograd and XLA, brought together for high-performance machine learning research.
FAIR's next-generation research platform for object detection and segmentation. It is a ground-up rewrite of the previous version, Detectron, and is powered by the PyTorch deep learning framework.
A deep learning framework developed with cleanliness, readability, and speed in mind.
A real-time multi-person keypoint detection library for body, face, hands, and foot estimation
Implementations of Machine Learning models from scratch in Python with a focus on transparency. Aims to showcase the nuts and bolts of ML in an accessible way.
The lightweight PyTorch wrapper for high-performance AI research.
Continually updated Data Science Python Notebooks: Spark, Hadoop MapReduce, HDFS, AWS, Kaggle, scikit-learn, matplotlib, pandas, NumPy, SciPy, and various command lines.
MLX is an array framework for machine learning on Apple silicon, developed by Apple machine learning research.
High-level wrapper built on the top of Pytorch which supports vision, text, tabular data and collaborative filtering.
Book/iPython notebooks on Probabilistic Programming in Python.
A framework for building industrial-strength applications with Transformer models and LLMs.
FAIR's software system that implements state-of-the-art object detection algorithms, including Mask R-CNN. It is written in Python and powered by the Caffe2 deep learning framework.
Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media
Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained
A framework for creating analytical web applications built on top of Plotly.js, React, and Flask
> Graph Neural Network Library for PyTorch.
A lightweight face recognition and facial attribute analysis (age, gender, emotion and race) framework for Python covering cutting-edge models such as VGG-Face, FaceNet, OpenFace, DeepFace, DeepID, Dlib and ArcFace.
Open source engineering platform to debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards. (Source Code)
Track, log, visualize and evaluate your LLM prompts and prompt chains.
Examples and best practices for building recommendation systems, provided as Jupyter notebooks. The repo contains some of the latest state of the art algorithms from Microsoft Research as well as from other companies and institutions.
An easier way to build neural search in the cloud. Compatible with Jupyter Notebooks.
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, JavaScript and more.
Code samples for my book "Neural Networks and Deep Learning".
The Computational Network Toolkit (CNTK) by Microsoft Research, is a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph.