semvec/pypi-packages huggingface dataset:
yadll 0.0.1
Yet Another Deep Learning Lab. Ultra light Deep Learning framework based on Theano
License: MIT

.. image:: https://travis-ci.org/pchavanne/yadll.svg
    :target: https://travis-ci.org/pchavanne/yadll

.. image:: https://coveralls.io/repos/github/pchavanne/yadll/badge.svg?branch=master
    :target: https://coveralls.io/github/pchavanne/yadll?branch=master

.. image:: https://img.shields.io/badge/license-MIT-blue.svg
    :target: https://github.com/pchavanne/yadll/blob/master/LICENSE

.. image:: https://readthedocs.org/projects/yadll/badge/
    :target: http://yadll.readthedocs.io/en/latest/


=====
Yadll
=====


**Y**\ et **a**\ nother **d**\ eep **l**\ earning **l**\ ab.

This is an ultra light deep learning framework written in Python and based on Theano_.
It allows you to very quickly start building Deep Learning models and play with toy examples.

If you are looking for a light deep learning API I would recommend using Lasagne_ or keras_ instead of yadll, both are mature, well documented and contributed projects.

Read the documentation at `Read the doc`_

.. _Theano: https://github.com/Theano/Theano
.. _`Theano's Deep Learning Tutorials`: http://deeplearning.net/tutorial/contents.html
.. _Lasagne: https://github.com/Lasagne/Lasagne
.. _keras: https://github.com/fchollet/keras
.. _blocks: https://github.com/mila-udem/blocks
.. _`Read the doc`: http://yadll.readthedocs.io/en/latest/


Its main features are:

* **Layers**:

  * Input Layer
  * Dropout Layer
  * Pool Layer
  * Conv Layer:

    * ConvPool Layer
  * Dense Layer:

    * Logistic Regression
    * Dropconnect
    * Unsupervised Layer:

      * Autoencoder (denoising autoencoder)
      * Restricted Boltzmann Machine
  * RNN
  * LSTM
  * GRU

* **Optimisation**:

  * Sgd
  * Momentum
  * Nesterov momentum
  * Adagrad
  * Adadelta
  * Rmsprop
  * Adam
  * Adamax



* **Hyperparameters grid search**

Installation
------------

.. code-block:: bash

  git clone git@github.com:pchavanne/yadll.git
  cd yadll
  pip install -e .

Example
-------

Different networks tested on mnist:

* Logisitic Regression
* Multi Layer Perceptron
* MLP with dropout
* MLP with dropconnect
* Conv Pool
* LeNet-5
* Autoencoder
* Denoising Autoencoder
* Gaussian Denoising Autoencoder
* Contractive Denoising Autoencoder
* Stacked Denoising Autoencoder
* Restricted Boltzmann Machine
* Deep Belief Network
* Recurrent Neural Networks
* Long Short-Term Memory
* Gated Recurrent unit

get the list of available networks:

.. code-block:: bash

  python mnist_dl.py --network_list


trainning a model for example lenet5:

.. code-block:: bash

  python mnist_dl.py lenet5


grid search on the hyperparameters:

.. code-block:: bash

  python hp_grid_search.py
snn1337 0.0.1
spiking neural network framework
License: MIT

[![Build Status](https://travis-ci.org/demikandr/snn1337-python-package.svg?branch=master)](https://travis-ci.org/demikandr/snn1337-python-package)

Spiking Neural Network 1337
===============================

version number: 0.0.1

Overview
--------

spiking neural network framework

Installation / Usage
--------------------

To install use pip(is not available yet):

    $ pip install snn1337

From repo with pip:

    $ git install https://github.com/demikandr/snn1337-python-package/archive/master.zip
    +Or from repo:

From repo if you're going to develop the package:

    $ git clone https://github.com/demikandr/snn1337-python-package.git
    $ sudo pip install -e .

Contributing
------------

TBD

Example
-------

TBD
gitreceive 0.2.2
UNIX command-line tool for python line-based stream processing
License: MIT
Keywords: google spreadsheet api util helper

**Author**: `Pahaz Blinov`_

**Repo**: https://github.com/pahaz/git-receive/

`git-receive` is a UNIX command-line tool for wrapping `git push`/`git pull`
command on a server side.

You can use it for `git push` action pipeline.
Example: `Static site PAAS`

**requirements**: Python3

.. _Pahaz Blinov: https://github.com/pahaz/
.. _Static site PAAS: https://github.com/pahaz/static-site-paas/