<commit_before>## Minimal neural networks library for educational purposes
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A pure Python and NumPy implementation of a neural networks library developed for educational purposes.

It focuses on readability rather than speed and thus aims at providing an easily understandable toy code, as opposed to the real production grade libraries.

##### Gradient checks
All analytical solutions are gradient checked with a numerical method (a center divided difference formula).

### Currently implemented
* [Fully connected layer](nnlib/layers/fully_connected.py)
* [Softmax with cross entropy loss layer](nnlib/layers/softmax.py)
* [ReLU family activations](nnlib/layers/relu.py)
* [Stochastic gradient descent optimizer](nnlib/optimizers/sgd.py)
* [L2 regularization](nnlib/regularizers.py)

### Future plans<commit_msg>update readme.md (add codebeat and codacy badges)<commit_after>## Minimal neural networks library for educational purposes
[![Build Status](https://travis-ci.org/inejc/nnlib.svg?branch=master)](https://travis-ci.org/inejc/nnlib)
[![codecov](https://codecov.io/gh/inejc/nnlib/branch/master/graph/badge.svg)](https://codecov.io/gh/inejc/nnlib)
[![codebeat badge](https://codebeat.co/badges/6bb37624-a748-4c41-bfd0-a3a7e787f212)](https://codebeat.co/projects/github-com-inejc-nnlib-master)
[![Codacy Badge](https://api.codacy.com/project/badge/Grade/e1aa9012832442e8849a125ae917f1a0)](https://www.codacy.com/app/inejc/nnlib?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=inejc/nnlib&amp;utm_campaign=Badge_Grade)

A pure Python and NumPy implementation of a neural networks library developed for educational purposes.

It focuses on readability rather than speed and thus aims at providing an easily understandable toy code, as opposed to the real production grade libraries.

##### Gradient checks
All analytical solutions are gradient checked with a numerical method (a center divided difference formula).

### Currently implemented
* [Fully connected layer](nnlib/layers/fully_connected.py)
* [Softmax with cross entropy loss layer](nnlib/layers/softmax.py)
* [ReLU family activations](nnlib/layers/relu.py)
* [Stochastic gradient descent optimizer](nnlib/optimizers/sgd.py)
* [L2 regularization](nnlib/regularizers.py)

### Future plans
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