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distributed with this work for additional information
regarding copyright ownership. The ASF licenses this file
to you under the Apache License, Version 2.0 (the
"License"); you may not use this file except in compliance
with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing,
software distributed under the License is distributed on an
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KIND, either express or implied. See the License for the
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Python Tutorials
=====
Getting started
---------------
.. container:: cards
.. card::
:title: A 60-minute Gluon crash course
:link: getting-started/crash-course/index.html
A quick overview of the core concepts of MXNet using the Gluon API.
.. card::
:title: Moving from other frameworks
:link: getting-started/to-mxnet/index.html
Guides that ease your transition to MXNet from other framework.
Packages & Modules
------------------
.. container:: cards
.. card::
:title: Gluon
:link: packages/gluon/index.html
MXNet's imperative interface for Python. If you're new to MXNet, start here!
.. card::
:title: NP and NPX
:link: packages/np/index.html
This section contains the `mxnet.np` and `mxnet.npx` usage hints.
.. card::
:title: Autograd API
:link: /api/python/docs/tutorials/packages/autograd/index.html
How to use Automatic Differentiation with the Autograd API.
Performance
-----------
.. container:: cards
.. card::
:title: Improving Performance
:link: performance/index.html
How to get the best performance from MXNet.
.. card::
:title: Profiler
:link: performance/backend/profiler.html
How to profile MXNet models.
.. card::
:title: Compression: int8
:link: performance/compression/int8.html
How to use int8 in your model to boost training speed.
.. card::
:title: MKL-DNN
:link: performance/backend/mkldnn/index.html
How to get the most from your CPU by using Intel's MKL-DNN.
.. card::
:title: TVM
:link: performance/backend/tvm.html
How to use TVM to boost performance.
Deployment
----------
.. container:: cards
.. card::
:title: MXNet on EC2
:link: deploy/run-on-aws/use_ec2.html
How to deploy MXNet on an Amazon EC2 instance.
.. card::
:title: MXNet on SageMaker
:link: deploy/run-on-aws/use_sagemaker.html
How to run MXNet using Amazon SageMaker.
..
PLACEHOLDER
.. card::
:title: Export
:link: deploy/export/index.html
How to export MXNet models.
.. card::
:title: C++
:link: deploy/inference/cpp.html
How to use MXNet models in a C++ environment.
.. card::
:title: Scala and Java
:link: deploy/inference/scala.html
How to use MXNet models in a Scala or Java environment.
PLACEHOLDER
..
Customization
-------------
.. container:: cards
Coming Soon (CustomOps and Custom Operators)
Next steps
----------
- To learn more about using MXNet to implement various deep learning algorithms
from scratch, we recommend the `Dive into Deep Learning
<https://d2l.ai>`_ book.
- Check out the `API Reference docs <../api/index.html>`_.
.. raw:: html
<style> h1 {display: none;} </style>
<style>.localtoc { display: none; }</style>
.. toctree::
:hidden:
:maxdepth: 3
getting-started/index
packages/index
performance/index
deploy/index
extend/index