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

Designing the architecture for the model


The main blocks of the model in this example will be the following:

  • First, the words of the input sentence are mapped to vectors of real numbers. This step is called vector representation of words or word embedding (for more details, see Chapter 3, Encoding Word into Vector).

  • Afterwards, this sequence of vectors is represented by one fixed-length and real-valued vector using a bi-LSTM encoder. This vector summarizes the input sentence and contains semantic, syntactic, and/or sentimental information based on the word vectors.

  • Finally, this vector is passed through a softmax classifier to classify the sentence into positive, negative, or neutral.

Vector representations of words

Word embeddings are an approach to distributional semantics that represents words as vectors of real numbers. Such a representation has useful clustering properties, since the words that are semantically and syntactically related are represented by similar vectors (see Chapter 3,...