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

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

An Engine-Agnostic Deep Learning Framework in Java

Open standard for machine learning interoperability

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

MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle

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

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

TensorLy: Tensor Learning in Python.

Probabilistic time series modeling in Python

A Great Collection of Deep Learning (e)Books

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

Deep Learning Inference benchmark. Supports OpenVINO™ toolkit, TensorFlow, TensorFlow Lite, ONNX Runtime, OpenCV DNN, MXNet, PyTorch, Apache TVM, ncnn, PaddlePaddle, etc.

A Deep Learning UCI-Chess Variant Engine written in C++ & Python 🦜

The Java implementation of Dive into Deep Learning (D2L.ai)

Machine Learning University: Accelerated Natural Language Processing Class

Machine Learning University: Accelerated Computer Vision Class

Machine Learning University: Accelerated Tabular Data Class

Distributed training framework for TensorFlow, Keras, PyTorch and Apache MXNet (standalone fork)

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