| .. Licensed to the Apache Software Foundation (ASF) under one |
| or more contributor license agreements. See the NOTICE file |
| 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 |
| "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY |
| KIND, either express or implied. See the License for the |
| specific language governing permissions and limitations |
| under the License. |
| |
| Crash Course |
| ============= |
| |
| This crash course will give you a quick overview of MXNet. You will review core concepts like NDArray (manipulating multiple dimensional arrays) and Gluon (create and train neural networks on CPU and GPU). The intended audience for this crash course is people already familiar with deep learning theory or other deep learning frameworks. For a deep dive into MXNet and deep learning architectures, please refer to [Dive Into Deep learning](http://d2l.ai/) textbook or [Introduction to Deep Learning Course](https://courses.d2l.ai/berkeley-stat-157/index.html) |
| |
| The course is structured in different sections that can be studied independently or as a whole. If you have a particular question you can consult only the section related to your question, but if you are new to the framework and have time, you can do the course from start to end. |
| |
| |
| .. toctree:: |
| :maxdepth: 1 |
| :caption: Contents |
| |
| 0-introduction |
| 1-nparray |
| 2-create-nn |
| 3-autograd |
| 4-components |
| 5-datasets |
| 6-train-nn |
| 7-use-gpus |
