neupy 0.4.0

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NeuPy v0.4.0
============

NeuPy is a Python library for Artificial Neural Networks. NeuPy supports many different types of Neural Networks from a simple perceptron to deep learning models.

.. image:: site/_static/img/mnist-solution-code.png
:width: 80%
:align: center

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

.. code-block:: bash

$ pip install neupy

User Guide
----------

* `Install NeuPy <installation.html>`_
* Check the `tutorials <tutorials.html>`_
* Learn more about NeuPy in the `documentation <documentation.html>`_
* Explore lots of different `neural network algorithms <cheatsheet.html>`_.
* Read `articles <http://neupy.com/archive.html>`_ and learn more about Neural Networks.

Links
-----

* `Tutorials <http://neupy.com/docs/tutorials.html>`_
* `Documentation <http://neupy.com>`_
* `Articles <http://neupy.com/archive.html>`_
* `Cheat sheet <http://neupy.com/docs/cheatsheet.html#algorithms>`_
* `Open Issues <https://github.com/itdxer/neupy/issues>`_

Articles
--------

* `Password recovery <http://neupy.com/2015/09/21/password_recovery.html>`_
* `Discrete Hopfield Network <http://neupy.com/2015/09/20/discrete_hopfield_network.html>`_
* `Predict prices for houses in the area of Boston <http://neupy.com/2015/07/04/boston_house_prices_dataset.html>`_
* `Visualize Backpropagation Algorithms <http://neupy.com/2015/07/04/visualize_backpropagation_algorithms.html>`_
* `MNIST classification <http://neupy.com/2016/11/12/mnist_classification.html>`_

Examples
--------

Convolutional Neural Networks
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

* `MNIST CNN <examples/cnn/mnist_cnn.py>`_
* `CIFAR10 CNN <examples/cnn/cifar10_cnn.py>`_
* `Pretrained AlexNet CNN <examples/cnn/alexnet.py>`_
* `Pretrained VGG16 CNN <examples/cnn/vgg16.py>`_
* `Pretrained VGG19 CNN <examples/cnn/vgg19.py>`_
* `SqueezeNet <examples/cnn/squeezenet.py>`_
* `GoogleNet <examples/cnn/googlenet.py>`_
* `Inception v3 <examples/cnn/inception_v3.py>`_
* `ResNet 50 <examples/cnn/resnet50.py>`_

Autoencoders
~~~~~~~~~~~~

* `MNIST, Denoising Autoencoder <examples/autoencoder/denoising_autoencoder.py>`_
* `MNIST, Convolutional Autoencoder <examples/autoencoder/conv_autoencoder.py>`_
* `MNIST, Stacked Convolutional Autoencoders <examples/autoencoder/stacked_conv_autoencoders.py>`_

Boltzmann Machine
~~~~~~~~~~~~~~~~~

* `Feature Learning from the MNIST Images, Restricted Boltzmann Machine (RBM) <examples/boltzmann_machine/rbm_mnist.py>`_
* `Gibbs sampling using face images, Restricted Boltzmann Machine (RBM) <examples/boltzmann_machine/rbm_faces_sampling.py>`_

MLP Neural Networks
~~~~~~~~~~~~~~~~~~~

* `MNIST, Multilayer perceptron <examples/mlp/mnist_mlp.py>`_
* `Rectangle images, Multilayer perceptron <examples/mlp/rectangles_mlp.py>`_
* `Boston House Price prediction, Hessian algorithm <examples/mlp/boston_price_prediction.py>`_
* `Learning Algorithms Visualization, Gradient Descent, Momentum, RPROP and Conjugate Gradient <examples/mlp/gd_algorithms_visualization.py>`_
* `IMDB review classification using CBOW and RPROP MLP <examples/mlp/imdb_review_classification>`_

Competitive Neural Networks
~~~~~~~~~~~~~~~~~~~~~~~~~~~

* `Simple SOFM example <examples/competitive/sofm_basic.py>`_

Neural Networks with Radial Basis Functions
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

* `Classify iris dataset, Probabilistic Neural Network (PNN) <examples/rbfn/pnn_iris.py>`_
* `Regression using Diabetes dataset, Generilized Neural Nerwork (GRNN) <examples/rbfn/grnn_params_selection.py>`_
* `Music-Speech audio classification, Probabilistic Neural Network (PNN) <examples/rbfn/music_speech>`_

Memory based Neural Networks
~~~~~~~~~~~~~~~~~~~~~~~~~~~~

* `Sinus function approximation, CMAC <examples/memory/cmac_basic.py>`_
* `Visualize Discrete Hopfield Neural Network energy function <examples/memory/dhn_energy_func.py>`_
* `Password recovery, Discrete Hopfield Neural Network <examples/memory/password_recovery.py>`_

Dependencies
------------

* Python 2.7, 3.4, 3.5
* Theano == 0.8.2
* NumPy >= 1.9.0
* SciPy >= 0.14.0
* Matplotlib >= 1.4.0
* graphviz == 0.5.1