mxnet

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

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

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

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.

Gluon CV Toolkit

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.

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

Probabilistic time series modeling in Python

An Engine-Agnostic Deep Learning Framework in Java

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.

Sandbox for training deep learning networks

🔮 A refreshing functional take on deep learning, compatible with your favorite libraries

Machine Learning University: Accelerated Natural Language Processing Class

A library for training and deploying machine learning models on Amazon SageMaker

AI on Hadoop

TensorLy: Tensor Learning in Python.

Machine Learning University: Accelerated Computer Vision Class

:computer: Learn to make machines learn so that you don't have to struggle to program them; The ultimate list

Machine Learning University: Accelerated Tabular Data Class

A high-performance ML model serving framework, offers dynamic batching and CPU/GPU pipelines to fully exploit your compute machine