<commit_before>Learninspy
==========

|Build Status|

### Welcome to learninspy!
Learninspy is a framework for building deep neural networks using Spark features on Python API (PySpark). The project was started in April 2015 by <NAME>, and it aims to exploit distributed computing capabilities provided by Spark to build and train neural networks with Deep Learning in a simple and flexible way. So, the key features pursued here are:
* **Simple and easy-to-follow:** That's why Python was chosen as the basis for developing on Spark, so this project must be easy to reuse in any application of deep learning.  
* **Extensible:** It has many degrees of freedom in its feature definitions (e.g. activation functions, optimization algorithms and stop criterions, hyper-parameters setup, etc) and of course in a simple way!.
* **Distributed flavor**: Taking advantage of Spark, the main power of learninspy lies in the distribution of both data pre-processing and optimization of neural networks. 

### Dependencies
* Python 2.7.x
* Spark[>=1.3.0]
* NumPy
* SciPy
* Matplotlib 

## Testing
To be defined...

### Important links
* Official repository of source: https://github.com/leferrad/learninspy/
* Documentation: http://learninspy.readthedocs.io/ (still in development)
* Contact: <EMAIL>

> DISCLAIMER: Documentation stuff **is in Spanish** due to an agreed scope.
<commit_msg>Update de README.md<commit_after>Learninspy
==========

[![Build Status](https://travis-ci.org/leferrad/learninspy.svg?branch=master)](https://travis-ci.org/leferrad/learninspy)

### Welcome to learninspy!
Learninspy is a framework for building deep neural networks using Spark features on Python API (PySpark). The project was started in April 2015 by <NAME>, and it aims to exploit distributed computing capabilities provided by Spark to build and train neural networks with Deep Learning in a simple and flexible way. So, the key features pursued here are:
* **Simple and easy-to-follow:** That's why Python was chosen as the basis for developing on Spark, so this project must be easy to reuse in any application of deep learning.  
* **Extensible:** It has many degrees of freedom in its feature definitions (e.g. activation functions, optimization algorithms and stop criterions, hyper-parameters setup, etc) and of course in a simple way!.
* **Distributed flavor**: Taking advantage of Spark, the main power of learninspy lies in the distribution of both data pre-processing and optimization of neural networks. 

### Dependencies
* Python 2.7.x
* Spark[>=1.3.0]
* NumPy
* SciPy
* Matplotlib 

## Testing
To be defined...

### Important links
* Official repository of source: https://github.com/leferrad/learninspy/
* Documentation: http://learninspy.readthedocs.io/ (still in development)
* Contact: <EMAIL>

> DISCLAIMER: Documentation stuff **is in Spanish** due to an agreed scope.

{'fraction_non_alphanumeric': 0.09084580577793247, 'fraction_numerical': 0.006265227984684998, 'mean_word_length': 4.806060606060606, 'pattern_counts': {'":': 0, '<': 7, '<?xml version=': 0, '>': 11, 'https://': 4, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '6042169', 'n_tokens_mistral': 814, 'n_tokens_neox': 736, 'n_words': 366}