Neurolab is a simple and powerful Neural Network Library for Python. Contains based neural networks, train algorithms and flexible framework to create and explore other networks.
Brian is easy to learn and use, highly flexible and easily extensible. The Brian package itself and simulations using it are all written in the Python programming language, which is an easy, concise and highly developed language with many advanced features and development tools, excellent documentation and a large community of users providing support and extension packages.
PyBrain is short for Python-Based Reinforcement Learning, Artificial Intelligence and Neural Network Library.
The theanets package is a deep learning and neural network toolkit. It is written in Python to interoperate with excellent tools like numpy and scikit-learn, and it uses Theano to accelerate computations when possible using your GPU.
pylearn2 is generally considered the library of choice for neural networks and deep learning in python. It’s designed for easy scientific experimentation rather than ease of use, so the learning curve is rather steep. Everything from standard Multilayer Perceptrons to Restricted Boltzmann Machines to Convolutional Nets to Autoencoders is provided.
Blocks is a framework that helps you build and manage neural network models on using Theano.
Deep neural network implementation without the learning cliff! This library implements multi-layer perceptrons, auto-encoders and (soon) recurrent neural networks with a stable Future Proof™ interface that’s compatible with scikit-learn for a more user-friendly and Pythonic interface.
Python module for performing basic dense linear algebra computations on the GPU using CUDA.
Stats Lies Truth 