| # Tutorials |
| |
| These tutorials introduce a few fundamental concepts in deep learning and how to implement them in _MXNet_. The _Basics_ section contains tutorials on manipulating arrays, building networks, loading/preprocessing data, etc. The _Training and Inference_ section talks about implementing Linear Regression, training a Handwritten digit classifier using MLP and CNN, running inferences using a pre-trained model, and lastly, efficiently training a large scale image classifier. |
| |
| ## Python |
| |
| ### Basics |
| |
| ```eval_rst |
| .. toctree:: |
| :maxdepth: 1 |
| |
| basic/ndarray |
| basic/symbol |
| basic/module |
| basic/data |
| ``` |
| |
| ### Training and Inference |
| |
| ```eval_rst |
| .. toctree:: |
| :maxdepth: 1 |
| |
| python/linear-regression |
| python/mnist |
| python/predict_image |
| vision/large_scale_classification |
| ``` |
| <br> |
| More tutorials and examples are available in the GitHub [repository](https://github.com/dmlc/mxnet/tree/master/example). |
| |
| ## Contributing Tutorials |
| |
| Want to contribute an MXNet tutorial? To get started, download the [tutorial template](https://github.com/dmlc/mxnet/tree/master/example/MXNetTutorialTemplate.ipynb). |
