Summary

The first concept is symbolic computing, which consists in building graph, that can be compiled and then executed wherever we decide in the Python code. A compiled graph is acting as a function that can be called anywhere in the code. The purpose of symbolic computing is to have an abstraction of the architecture on which the graph will be executed, and which libraries to compile it with. As presented, symbolic variables are typed for the target architecture during compilation.

The second concept is the tensor, and the operators provided to manipulate tensors. Most of these were already available in CPU-based computation libraries, such as NumPy or SciPy. They have simply been ported to symbolic computing, requiring their equivalents on GPU. They use underlying acceleration libraries, such as BLAS, Nvidia Cuda, and cuDNN.

The last concept introduced by Theano is automatic differentiation—a very useful feature in deep learning to backpropagate errors and adjust the weights following the gradients, a process known as gradient descent. Also, the scan operator enables us to program loops (while..., for...,) on the GPU, and, as other operators, available through backpropagation as well, simplifying the training of models a lot.

We are now ready to apply this to deep learning in the next few chapters and have a look at this knowledge in practice.