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


Classification is a very wide topic in machine learning. It consists of predicting a class or a category, as we have shown with our handwritten digits example. In Chapter 7, Classifying Images with Residual Networks, we'll see how to classify a wider set of natural images and objects.

Classification can be applied to different problems and the cross-entropy/negative log likelihood is the common loss function to solve them through gradient descent. There are many other loss functions for problems such as regression (mean square error loss) or unsupervised joint learning (hinge loss).

In this chapter, we have been using a very simple update rule as gradient descent named stochastic gradient descent, and presented some other gradient descent variants (Momentum, Nesterov, RMSprop, ADAM, ADAGRAD, ADADELTA). There has been some research into second order optimizations, such as Hessian Free, or K-FAC, which provided better results in deep or recurrent networks but remain complex and costly...