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

Encoding and embedding


Each word can be represented by an index in a vocabulary:

Encoding words is the process of representing each word as a vector. The simplest method of encoding words is called one-hot or 1-of-K vector representation. In this method, each word is represented as an vector with all 0s and one 1 at the index of that word in the sorted vocabulary. In this notation, |V| is the size of the vocabulary. Word vectors in this type of encoding for vocabulary {King, Queen, Man, Woman, Child} appear as in the following example of encoding for the word Queen:

In the one-hot vector representation method, every word is equidistant from the other. However, it fails to preserve any relationship between them and leads to data sparsity. Using word embedding does overcome some of these drawbacks.

Word embedding is an approach to distributional semantics that represents words as vectors of real numbers. Such representation has useful clustering properties, since it groups together words that...