<commit_before>tiny-cnn: A header only, dependency-free deep learning framework in C++11
========

| **Linux/Mac OS** | **Windows** |
|------------------|-------------|
|[![Build Status](https://travis-ci.org/nyanp/tiny-cnn.svg?branch=master)](https://travis-ci.org/nyanp/tiny-cnn)|[![Build status](https://ci.appveyor.com/api/projects/status/s4mow1544tvoqeeu?svg=true)](https://ci.appveyor.com/project/nyanp/tiny-cnn)|

tiny-cnn is a C++11 implementation of deep learning. It is suitable for deep learning on limited computational resource, embedded systems and IoT devices.

* [Features](#features)
* [Comparison with other libraries](#comparison-with-other-libraries)
* [Supported networks](#supported-networks)
* [Dependencies](#dependencies)
* [Build](#build)
* [Examples](#examples)
* [References](#references)
* [License](#license)
* [Mailing list](#mailing-list)

see [Wiki Pages](https://github.com/nyanp/tiny-cnn/wiki) for more info.

## Features
- reasonably fast, without GPU
    - with TBB threading and SSE/AVX vectorization
    - 98.8% accuracy on MNIST in 13 minutes training (@Core i7-3520M)
- portable & header-only
    - Run anywhere only if only you have a compiler which supports C++11
    - Just include tiny_cnn.h and write your model in c++. There is nothing to install.
- easy to integrate with a real application
    - no output on stdout/stderr
    - a constant throughput (simple parallelization model, no garbage collection)
    - work without exception
    - [can import caffe's model](https://github.com/nyanp/tiny-cnn/tree/master/examples/caffe_converter)
- simple implementation
    - be a good library for learning neural networks<commit_msg>change the pointer to the documentation<commit_after>tiny-cnn: A header only, dependency-free deep learning framework in C++11
========

| **Linux/Mac OS** | **Windows** |
|------------------|-------------|
|[![Build Status](https://travis-ci.org/nyanp/tiny-cnn.svg?branch=master)](https://travis-ci.org/nyanp/tiny-cnn)|[![Build status](https://ci.appveyor.com/api/projects/status/s4mow1544tvoqeeu?svg=true)](https://ci.appveyor.com/project/nyanp/tiny-cnn)|

tiny-cnn is a C++11 implementation of deep learning. It is suitable for deep learning on limited computational resource, embedded systems and IoT devices.

* [Features](#features)
* [Comparison with other libraries](#comparison-with-other-libraries)
* [Supported networks](#supported-networks)
* [Dependencies](#dependencies)
* [Build](#build)
* [Examples](#examples)
* [References](#references)
* [License](#license)
* [Mailing list](#mailing-list)

Check out the [documentation](doc/readme.md) for more info.

## Features
- reasonably fast, without GPU
    - with TBB threading and SSE/AVX vectorization
    - 98.8% accuracy on MNIST in 13 minutes training (@Core i7-3520M)
- portable & header-only
    - Run anywhere only if only you have a compiler which supports C++11
    - Just include tiny_cnn.h and write your model in c++. There is nothing to install.
- easy to integrate with a real application
    - no output on stdout/stderr
    - a constant throughput (simple parallelization model, no garbage collection)
    - work without exception
    - [can import caffe's model](https://github.com/nyanp/tiny-cnn/tree/master/examples/caffe_converter)
- simple implementation
    - be a good library for learning neural networks
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