Visualizer for neural network, deep learning and machine learning models

Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

State-of-the-art 2D and 3D Face Analysis Project

ncnn is a high-performance neural network inference framework optimized for the mobile platform

Open standard for machine learning interoperability

The Unified AI Framework

Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.

深度学习入门教程, 优秀文章, Deep Learning Tutorial

State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.

Setup and customize deep learning environment in seconds.

MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.

Gluon CV Toolkit

A GPU-accelerated library containing highly optimized building blocks and an execution engine for data processing to accelerate deep learning training and inference applications.

TensorFlow Basic Tutorial Labs

In this repository, I will share some useful notes and references about deploying deep learning-based models in production.

This project reproduces the book Dive Into Deep Learning (https://d2l.ai/), adapting the code from MXNet into PyTorch.

Probabilistic time series modeling in Python

An Engine-Agnostic Deep Learning Framework in Java

A high performance and generic framework for distributed DNN training