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| <h1 class="post-title">Ecosystem</h1> |
| <h3>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.</h3><a style="float:left; margin-top:20px" href="/versions/1.9.1/get_started" class="btn btn-action">Get Started |
| <span class="span-accented">›</span></a></header> |
| |
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| |
| <div class="ecosystem-page"> |
| <div class="row"> |
| <h2>D2L.ai</h2> |
| <div class="row"> |
| <div class="col-4"> |
| <a href="http://d2l.ai/"><img src="/versions/1.9.1/assets/img/front.jpg"></a> |
| </div> |
| <div class="col-8"> |
| <p>A <a href="https://d2l.ai">deep learning book</a> with interactive jupyter notebooks, math formula, |
| and a dedicated forum for discussions.</p> |
| <p>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.</p> |
| <p>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.</p> |
| <p>The book is authored by <a href="https://www.astonzhang.com/">Aston Zhang</a>, Amazon Applied |
| Scientist UIUC Ph.D., <a href="http://zacklipton.com/">Zack C. Lipton</a>, CMU Assistant Professor |
| UCSD Ph.D., |
| <a href="https://scholar.google.com/citations?user=Z_WrhK8AAAAJ&hl=en">Mu Li</a> Amazon Principal |
| Scientist CMU Ph.D. and <a href="https://alex.smola.org/">Alex J. Smola</a> Amazon VP/Distinguished |
| Scientist TU Berlin Ph.D. |
| <p>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</p> |
| </div> |
| </div> |
| </div> |
| <br><br> |
| <h2>Toolkits</h2> |
| <div class="row"><div class="col-4"> |
| <div class="card"> |
| <a href="https://gluon-cv.mxnet.io"> |
| <div class="card-text"> |
| <div class="card-header-title"> |
| <h4>GluonCV</h4> |
| <img src="/versions/1.9.1/assets/img/visual.svg"> |
| </div> |
| <p class="card-summary">GluonCV is a computer vision toolkit with rich model zoo. From object detection to pose estimation.</p> |
| </div> |
| </a> |
| </div> |
| </div><div class="col-4"> |
| <div class="card"> |
| <a href="https://gluon-nlp.mxnet.io/"> |
| <div class="card-text"> |
| <div class="card-header-title"> |
| <h4>GluonNLP</h4> |
| <img src="/versions/1.9.1/assets/img/artificial-intelligence.svg"> |
| </div> |
| <p class="card-summary">GluonNLP provides state-of-the-art deep learning models in NLP. For engineers and researchers to fast prototype research ideas and products.</p> |
| </div> |
| </a> |
| </div> |
| </div><div class="col-4"> |
| <div class="card"> |
| <a href="https://gluon-ts.mxnet.io/"> |
| <div class="card-text"> |
| <div class="card-header-title"> |
| <h4>GluonTS</h4> |
| <img src="/versions/1.9.1/assets/img/line-graph.svg"> |
| </div> |
| <p class="card-summary">Gluon Time Series (GluonTS) is the Gluon toolkit for probabilistic time series modeling, focusing on deep learning-based models.</p> |
| </div> |
| </a> |
| </div> |
| </div><div class="col-4"> |
| <div class="card"> |
| <a href="https://autogluon.mxnet.io"> |
| <div class="card-text"> |
| <div class="card-header-title"> |
| <h4>AutoGluon</h4> |
| <img src="/versions/1.9.1/assets/img/autogluon.png"> |
| </div> |
| <p class="card-summary">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.</p> |
| </div> |
| </a> |
| </div> |
| </div></div> |
| <br><br> |
| <h2>Ecosystem</h2> |
| <div class="row"><div class="col-3"> |
| <div class="card"> |
| <a href="https://github.com/NervanaSystems/coach"> |
| <div class="card-text"> |
| <div class="card-header-title"> |
| <h4>Coach RL</h4> |
| <img src="/versions/1.9.1/assets/img/coach_logo.png"> |
| </div> |
| <p class="card-summary">Coach is a python reinforcement learning framework containing implementation of many state-of-the-art algorithms, it supports Apache MXNet as a back-end</p> |
| </div> |
| </a> |
| </div> |
| </div><div class="col-3"> |
| <div class="card"> |
| <a href="https://www.dgl.ai/"> |
| <div class="card-text"> |
| <div class="card-header-title"> |
| <h4>Deep Graph Library</h4> |
| <img src=""> |
| </div> |
| <p class="card-summary">DGL is a Python package dedicated to deep learning on graphs supporting Apache MXNet as a backend.</p> |
| </div> |
| </a> |
| </div> |
| </div><div class="col-3"> |
| <div class="card"> |
| <a href="https://gluon-face.readthedocs.io/en/latest/"> |
| <div class="card-text"> |
| <div class="card-header-title"> |
| <h4>GluonFR</h4> |
| <img src=""> |
| </div> |
| <p class="card-summary">Community-driven toolkit for Face Recognition and Face Detection</p> |
| </div> |
| </a> |
| </div> |
| </div><div class="col-3"> |
| <div class="card"> |
| <a href="https://github.com/deepinsight/insightface"> |
| <div class="card-text"> |
| <div class="card-header-title"> |
| <h4>InsightFace</h4> |
| <img src=""> |
| </div> |
| <p class="card-summary">State-of-the-art face detection and face recognition repository, including ArcFace loss and RetinaFace implementation</p> |
| </div> |
| </a> |
| </div> |
| </div><div class="col-3"> |
| <div class="card"> |
| <a href="https://github.com/awslabs/keras-apache-mxnet"> |
| <div class="card-text"> |
| <div class="card-header-title"> |
| <h4>Keras-MXNet</h4> |
| <img src="/versions/1.9.1/assets/img/keras.png"> |
| </div> |
| <p class="card-summary">Keras-MXNet provides a backend support for the widely used high level API Keras.</p> |
| </div> |
| </a> |
| </div> |
| </div><div class="col-3"> |
| <div class="card"> |
| <a href="https://github.com/awslabs/mxboard"> |
| <div class="card-text"> |
| <div class="card-header-title"> |
| <h4>MXBoard</h4> |
| <img src=""> |
| </div> |
| <p class="card-summary">MXBoard provides a set of APIs for logging Apache MXNet data for visualization in TensorBoard.</p> |
| </div> |
| </a> |
| </div> |
| </div><div class="col-3"> |
| <div class="card"> |
| <a href="https://mxfusion.readthedocs.io/en/master/index.html"> |
| <div class="card-text"> |
| <div class="card-header-title"> |
| <h4>MXFusion</h4> |
| <img src="/versions/1.9.1/assets/img/mxfusion.png"> |
| </div> |
| <p class="card-summary">MXFusion is a modular deep probabilistic programming library. It lets you use state-of-the-art inference techniques for specialized probabilistic models.</p> |
| </div> |
| </a> |
| </div> |
| </div><div class="col-3"> |
| <div class="card"> |
| <a href="https://github.com/awslabs/mxnet-model-server"> |
| <div class="card-text"> |
| <div class="card-header-title"> |
| <h4>MXNet Model Server</h4> |
| <img src=""> |
| </div> |
| <p class="card-summary">Model Server for Apache MXNet (MMS) is a flexible and easy to use tool for serving deep learning models exported from Apache MXNet or the Open Neural Network Exchange (ONNX).</p> |
| </div> |
| </a> |
| </div> |
| </div><div class="col-3"> |
| <div class="card"> |
| <a href="https://awslabs.github.io/sockeye/"> |
| <div class="card-text"> |
| <div class="card-header-title"> |
| <h4>Sockeye</h4> |
| <img src=""> |
| </div> |
| <p class="card-summary">Sockeye is a sequence-to-sequence framework for Neural Machine Translation based on Apache MXNet. It implements state-of-the-art encoder-decoder architectures.</p> |
| </div> |
| </a> |
| </div> |
| </div><div class="col-3"> |
| <div class="card"> |
| <a href="http://tensorly.org/stable/home.html"> |
| <div class="card-text"> |
| <div class="card-header-title"> |
| <h4>TensorLy</h4> |
| <img src="/versions/1.9.1/assets/img/tensorly_logo.png"> |
| </div> |
| <p class="card-summary">TensorLy is a high level API for tensor methods and deep tensorized neural networks in Python that aims to make tensor learning simple.</p> |
| </div> |
| </a> |
| </div> |
| </div><div class="col-3"> |
| <div class="card"> |
| <a href="https://tvm.ai/about"> |
| <div class="card-text"> |
| <div class="card-header-title"> |
| <h4>Apache TVM</h4> |
| <img src="/versions/1.9.1/assets/img/tvm.png"> |
| </div> |
| <p class="card-summary">Apache TVM is an open deep learning compiler stack for CPUs, GPUs, and specialized accelerators. It supports a number of framework including Apache MXNet.</p> |
| </div> |
| </a> |
| </div> |
| </div><div class="col-3"> |
| <div class="card"> |
| <a href="https://xfer.readthedocs.io/en/master/"> |
| <div class="card-text"> |
| <div class="card-header-title"> |
| <h4>XFer</h4> |
| <img src="/versions/1.9.1/assets/img/xfer.png"> |
| </div> |
| <p class="card-summary">Xfer is a library that allows quick and easy transfer of knowledge stored in deep neural networks implemented in Apache MXNet.</p> |
| </div> |
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