Book Image

Deep Learning with Theano

By : Christopher Bourez
Book Image

Deep Learning with Theano

By: Christopher Bourez

Overview of this book

This book offers a complete overview of Deep Learning with Theano, a Python-based library that makes optimizing numerical expressions and deep learning models easy on CPU or GPU. The book provides some practical code examples that help the beginner understand how easy it is to build complex neural networks, while more experimented data scientists will appreciate the reach of the book, addressing supervised and unsupervised learning, generative models, reinforcement learning in the fields of image recognition, natural language processing, or game strategy. The book also discusses image recognition tasks that range from simple digit recognition, image classification, object localization, image segmentation, to image captioning. Natural language processing examples include text generation, chatbots, machine translation, and question answering. The last example deals with generating random data that looks real and solving games such as in the Open-AI gym. At the end, this book sums up the best -performing nets for each task. While early research results were based on deep stacks of neural layers, in particular, convolutional layers, the book presents the principles that improved the efficiency of these architectures, in order to help the reader build new custom nets.
Table of Contents (22 chapters)
Deep Learning with Theano
Credits
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Index

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.