.. NumpyDL documentation master file, created by sphinx-quickstart on Mon Apr 10 13:33:52 2017. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. Hi, NumpyDL =========== NumpyDL is a simple deep learning library based on pure Python/Numpy. NumpyDL is a work in progress, input is welcome. The project is on `GitHub `_. The main features of NumpyDL are as follows: * *Pure* in Numpy and *native* to Python * Support basic *automatic differentiation* * Support *commonly used models*, such as MLP, RNNs, GRUs, LSTMs and CNNs * *Perfect documents* and easy to learn deep learning knowledge * Flexible network configurations and learning algorithms. * API like Keras deep learning library The design of NumpyDL is governed by several principles: * **Simplicity**: Be easy to use, easy to understand and easy to extend, to facilitate use in research. Interfaces should be kept small, with as few classes and methods as possible. Every added abstraction and feature should be carefully scrutinized, to determine whether the added complexity is justified. * **Transparency**: Native to Numpy, directly process and return Python/Numpy data types. Do not rely on the functionality of Theano, Tensorflow or any such deep learning frameworks. * **Modularity**: Allow all parts (layers, regularizers, optimizers, ...) to be used independently of NumpyDL. Make it easy to use components in isolation or in conjunction with other frameworks. * **Focus**: “Do one thing and do it well”. Do not try to provide a library for everything to do with deep learning. User Guides =========== The NumpyDL user guide explains how to install NumpyDL, how to build and train neural networks using NumpyDL, and how to contribute to the library as a developer. .. toctree:: :maxdepth: 2 user_guide/installation Tutorials ========= This is the tutorials of `NumpDL `_. .. toctree:: :maxdepth: 2 tutorials/activations tutorials/MLP tutorials/CNN_Part1 tutorials/CNN_Part2 tutorials/RNN_part1 Examples ======== This part provides examples for building deep neural networks. .. toctree:: :maxdepth: 2 API Reference ============= If you are looking for information on a specific function, class or method, this part of the documentation is for you. .. toctree:: :maxdepth: 2 api_reference/activation api_reference/initialization api_reference/objectives api_reference/optimizers api_reference/model Indices and tables ================== * :ref:`genindex` * :ref:`modindex` * :ref:`search` .. _GitHub: https://github.com/oujago/NumpyDL