Theano Tutorial (Pascal Lamblin, MILA)
Pascal Lamblin, Hugo Larochelle, Andrej Karpathy, Richard Socher, Sherry Moore, Ruslan Salakhutdinov, Andrew Ng, John Schulman, Adam Coates, Alex Wiltschko, Quoc Le, Yoshua Bengio, Shubho Sengupta
This presentation details Theano, an eight-year-old symbolic expression compiler that enables high-performance deep learning by automatically differentiating mathematical graphs and compiling them into optimized C++ or CUDA code. The session demonstrates the framework's core capabilities, including graph manipulation for neural network backpropagation, GPU acceleration via shared variables, and sequence modeling through the `scan` operator, while showcasing practical implementations of logistic regression, convolutional networks, and LSTMs on datasets like MNIST. Addressing deployment challenges inherent in its tight Python integration, the discussion concludes by highlighting Docker containers as the standard solution for distributing models and outlines a roadmap for enhanced 3D convolution and cuDNN support.