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bigdl

BigDL: Distributed Deep Learning Library for Apache Spark

caffe

Caffe: a fast open framework for deep learning.

caffe-onnx

caffe model convert to onnx model

cpuinfo

CPU INFOrmation library (x86/x86-64/ARM/ARM64, Linux/Windows/Android/macOS/iOS)

custom-op

Guide for building custom op for TensorFlow

darknet

Convolutional Neural Networks

deepinterestnetwork

detectron

FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.

detectron2

Detectron2 is FAIR's next-generation research platform for object detection and segmentation.

dldt

OpenVINO™ Toolkit - Deep Learning Deployment Toolkit repository

dlrm

An implementation of a deep learning recommendation model (DLRM)

glow

Compiler for Neural Network hardware accelerators

incubator-mxnet

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

incubator-tvm

Open deep learning compiler stack for cpu, gpu and specialized accelerators

learining

linux

Linux kernel source tree

ngraph

nGraph - open source C++ library, compiler and runtime for Deep Learning

onnx

Open Neural Network Exchange

personal_recommendation-master

The personalized recommendation system is an intelligent platform based on massive data mining. It can simulate store sales personnel to provide product information and suggestions to customers, and provide fully personalized decision support and information services for customers' shopping. Its goal is to Satisfying the needs of users, meeting the needs that users are not aware of, or realizing, but not expressing the needs, allowing users to go beyond the individual's vision and avoid seeing the trees without seeing the forest. A good recommendation system can greatly increase user loyalty and bring huge benefits to e-commerce. Personalized recommendation is to recommend information and products of interest to users according to their interests and purchasing behavior. As the scale of e-commerce continues to expand, the number and variety of products grow rapidly, and customers need to spend a lot of time to find the products they want to buy. This kind of browsing of a large amount of unrelated information and product processes will undoubtedly cause consumers who are drowning in information overload problems to continue to lose. In order to solve these problems, a personalized recommendation system came into being. The recommendation system is a branch of data mining. It is a special data mining system, which is mainly reflected in the real-time and interactivity of the recommendation system. The system recommends information that meets the interests of the user according to the user's interests, also known as the personalized recommendation system. It not only based on the user's past history, but also needs to react in real time with the behavior of the current period of time, and correct and optimize the recommendation result according to the feedback result of interaction with the user.

pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

tensorflow

An Open Source Machine Learning Framework for Everyone

tensorflow-onnx

Convert TensorFlow models to ONNX

tvm

TVM integration into PyTorch

vocdevkit

http://host.robots.ox.ac.uk/pascal/VOC/index.html

xla

Enabling PyTorch on Google TPU