--- layout: page title: Ecosystem subtitle: Explore a rich ecosystem of libraries, tools, and more to support research and development of Deep Learning application across many fields and domains of application. action: Get Started action_url: /get_started permalink: /ecosystem/ ecosystem_toolkits: - title: GluonCV text: GluonCV is a computer vision toolkit with rich model zoo. From object detection to pose estimation. icon: /assets/img/visual.svg link: https://gluon-cv.mxnet.io - title: GluonNLP text: GluonNLP provides state-of-the-art deep learning models in NLP. For engineers and researchers to fast prototype research ideas and products. icon: /assets/img/artificial-intelligence.svg link: https://gluon-nlp.mxnet.io/ - title: GluonTS text: Gluon Time Series (GluonTS) is the Gluon toolkit for probabilistic time series modeling, focusing on deep learning-based models. icon: /assets/img/line-graph.svg link: https://gluon-ts.mxnet.io/ - title: AutoGluon text: AutoGluon enables easy-to-use and easy-to-extend AutoML with a focus on deep learning and real-world applications spanning image, text, or tabular data. icon: /assets/img/autogluon.png link: https://autogluon.mxnet.io ecosystem_other: - title: Coach RL text: Coach is a python reinforcement learning framework containing implementation of many state-of-the-art algorithms, it supports MXNet as a back-end icon: /assets/img/coach_logo.png link: https://github.com/NervanaSystems/coach - title: Deep Graph Library text: DGL is a Python package dedicated to deep learning on graphs supporting MXNet as a backend. link: https://www.dgl.ai/ - title: GluonFR text: Community-driven toolkit for Face Recognition and Face Detection link: https://gluon-face.readthedocs.io/en/latest/ - title: InsightFace text: State-of-the-art face detection and face recognition repository, including ArcFace loss and RetinaFace implementation link: https://github.com/deepinsight/insightface - title: Keras-MXNet text: Keras-MXNet provides a backend support for the widely used high level API Keras. link: https://github.com/awslabs/keras-apache-mxnet icon: /assets/img/keras.png - title: MXBoard text: MXBoard provides a set of APIs for logging MXNet data for visualization in TensorBoard. link: https://github.com/awslabs/mxboard - title: MXFusion text: MXFusion is a modular deep probabilistic programming library. It lets you use state-of-the-art inference techniques for specialized probabilistic models. icon: /assets/img/mxfusion.png link: https://mxfusion.readthedocs.io/en/master/index.html - title: MXNet Model Server text: Model Server for Apache MXNet (MMS) is a flexible and easy to use tool for serving deep learning models exported from MXNet or the Open Neural Network Exchange (ONNX). link: https://github.com/awslabs/mxnet-model-server - title: Sockeye text: Sockeye is a sequence-to-sequence framework for Neural Machine Translation based on Apache MXNet Incubating. It implements state-of-the-art encoder-decoder architectures. link: https://awslabs.github.io/sockeye/ - title: TensorLy text: TensorLy is a high level API for tensor methods and deep tensorized neural networks in Python that aims to make tensor learning simple. icon: /assets/img/tensorly_logo.png link: http://tensorly.org/stable/home.html - title: TVM text: TVM is an open deep learning compiler stack for CPUs, GPUs, and specialized accelerators. It supports a number of framework including MXNet. link: https://tvm.ai/about icon: /assets/img/tvm.png - title: XFer text: Xfer is a library that allows quick and easy transfer of knowledge stored in deep neural networks implemented in MXNet. link: https://xfer.readthedocs.io/en/master/ icon: /assets/img/xfer.png - title: DJL text: Deep Java Library is an open source library to build and deploy deep learning in Java icon: /assets/img/djl.png link: https://djl.ai/ ---

D2L.ai

A deep learning book with interactive jupyter notebooks, math formula, and a dedicated forum for discussions.

It offers an interactive learning experience with mathematics, figures, code, text, and discussions, where concepts and techniques are illustrated and implemented with experiments on real data sets.

Each section is an executable Jupyter notebook. You can modify the code and tune hyperparameters to get instant feedback to accumulate practical experiences in deep learning.

The book is authored by Aston Zhang, Amazon Applied Scientist UIUC Ph.D., Zack C. Lipton, CMU Assistant Professor UCSD Ph.D., Mu Li Amazon Principal Scientist CMU Ph.D. and Alex J. Smola Amazon VP/Distinguished Scientist TU Berlin Ph.D.

D2L is used as a textbook or a reference book at Carnegie Mellon University, Georgia Institute of Technology, the University of California Berkeley and many more university



Toolkits

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Ecosystem

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