mxnet

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 400 universities from 60 countries including Stanford, MIT, Harvard, and Cambridge.

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

Open standard for machine learning interoperability

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

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

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

The Unified Machine Learning Framework

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

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

TensorFlow Basic Tutorial Labs

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

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

A high performance and generic framework for distributed DNN training

MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle