Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
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Updated
May 22, 2024 - Python
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
Open standard for machine learning interoperability
The Unified AI Framework
Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
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
Probabilistic time series modeling in Python
A high performance and generic framework for distributed DNN training
A Simple and Versatile Framework for Object Detection and Instance Recognition
Sandbox for training deep learning networks
🔮 A refreshing functional take on deep learning, compatible with your favorite libraries
NLP made easy
Reinforcement Learning Coach by Intel AI Lab enables easy experimentation with state of the art Reinforcement Learning algorithms
A library for training and deploying machine learning models on Amazon SageMaker
TensorLy: Tensor Learning in Python.
《深度学习与计算机视觉》配套代码
AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet.
A high-performance ML model serving framework, offers dynamic batching and CPU/GPU pipelines to fully exploit your compute machine
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