An Open Source Machine Learning Framework for Everyone

TFDS is a collection of datasets ready to use with TensorFlow, Jax, ...

Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.

🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.

Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

A flexible, high-performance serving system for machine learning models

Machine Learning Containers for NVIDIA Jetson and JetPack-L4T

Standardized Serverless ML Inference Platform on Kubernetes

A set of Docker images that include popular frameworks for machine learning, data science and visualization.

ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator

TensorFlow documentation

Notebooks 📔

Code for intrusion detection system based on "Intrusion Detection System Using Machine Learning Algorithms" tutorial on Geeksforgeeks and Intrusion Detection on NSL KDD Github repository.

Implementing a Denoising Diffsuion Probabilistic Model (DDPM) on Tensorflow from scratch for Pokémon sprites synthesis

Database for AI. Store Vectors, Images, Texts, Videos, etc. Use with LLMs/LangChain. Store, query, version, & visualize any AI data. Stream data in real-time to PyTorch/TensorFlow. https://activeloop.ai

An Engine-Agnostic Deep Learning Framework in Java

A Raspberry Pi camera for taking pictures using object detection.

Easily implement parallel training and distributed training. Machine learning library. Note.neuralnetwork.tf package include Llama2, Llama3, CLIP, ViT, ConvNeXt, SwiftFormer, etc, these models built with Note are compatible with TensorFlow and can be trained with TensorFlow.

AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet.