Chapter 1. Theano Basics
This chapter presents Theano as a compute engine and the basics for symbolic computing with Theano. Symbolic computing consists of building graphs of operations that will be optimized later on for a specific architecture, using the computation libraries available for this architecture.
Although this chapter might appear to be a long way from practical applications, it is essential to have an understanding of the technology for the following chapters; what is it capable of and what value does it bring? All the following chapters address the applications of Theano when building all possible deep learning architectures.
Theano may be defined as a library for scientific computing; it has been available since 2007 and is particularly suited to deep learning. Two important features are at the core of any deep learning library: tensor operations, and the capability to run the code on CPU or Graphical Computation Unit (GPU). These two features enable us to work with a massive amount of multi-dimensional data. Moreover, Theano proposes automatic differentiation, a very useful feature that can solve a wider range of numeric optimizations than deep learning problems.
The chapter covers the following topics:
- Theano installation and loading
- Tensors and algebra
- Symbolic programming
- Graphs
- Automatic differentiation
- GPU programming
- Profiling
- Configuration
