#Author - Ether Wei

###Pre-note

• Docs can be found in \doc
• The files in the directory \deprecated are from older versions.

###Introduction:

• It's a theano-based neural-network.
• Required modulas include: numpy, theano.
• It's a little bit complicated to learn to use at first.
• Before you start using the nnet, plz read the Rules & Cautions blocks below.

###Version notes In this version, I have replaced the old signal.conv2d. Because though this version provides you with more flexibilities, it's considerably slower than T.nnet.conv2d. And also, I have restrained the rules.

###Future Improvements

• RNN
• Restricted Boltzmann Machine
• Auto-encoder
• Sparse coding
• Combination of models.

##Notes From Author:

• Neural network is really tough to learn.
• The key part in bulding a nnet is in selecting proper parameters for layers.
• And also, if time premitted, grabing a book about numpy is a really good choice.
• I used the \clearPyc.py to clean the pyc files created by python interpreter before pushing to git.

##Cautions

• It's strongly recommended to read the doc before using this project.
• If you have never touched numpy or theano before, I suggest you to try them out yourself a little bit. It's because sometimes when you find a bug, having some knowledge of them can help great lot.

##Rules

• The input to the nnet should contain a dimension represent the batch size.
• The mini-batch isn't supported. However, you may see some implementations of the layers support the mini-batch, it's not supported in the optimizer. Thought this could mean that the computation could be slower, the mini-batch can be simulated anyway.

##Acknowleges I have learned a lot from deeplearning.net, part of the library is inspired by their work.