Theano is a Python library that defines a set of mathematical functions for building deep nets. Nets that use these functions as their building blocks will be highly optimized for training.

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The core feature of Theano is the use of vectors and matrices for all of its functions. Vectorized code runs quickly since multiple values can be processed in parallel. Since Deep Nets require large amounts of computation throughout the training process, vectorization is a highly-recommended feature. Theano is multi-threaded with GPU support, so deep nets can be trained on just a single machine within a reasonable amount of time.

To use Theano for Deep Learning, you must code every aspect of a deep net including the layers, the nodes, the activation, and the training rate. However, all the functions that run your code will be vectorized, resulting in an efficient implementation. Many software libraries extend Theano, making it easier to use in your projects. The Blocks library helps by parameterizing Theano functions. The Lasagne library allows you to specify hyper-parameters in order to build a net layer by layer. Niche libraries like Passage help implement recurrent nets for text analysis.

Do you have experience coding neural nets with the Theano library? Please comment and share your thoughts.

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