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Deep Learning with Theano

You're reading from Deep Learning with Theano Perform large-scale numerical and scientific computations efficiently

Product type Paperback

Published in Jul 2017

Publisher Packt

ISBN-13 9781786465825

Length 300 pages

Edition 1st Edition

Tools

Theano

Concepts

Deep Learning

Author (1):

Christopher Bourez

View More author details

Table of Contents (15) Chapters

Preface

1. Theano Basics FREE CHAPTER

2. Classifying Handwritten Digits with a Feedforward Network

3. Encoding Word into Vector

4. Generating Text with a Recurrent Neural Net

5. Analyzing Sentiment with a Bidirectional LSTM

6. Locating with Spatial Transformer Networks

7. Classifying Images with Residual Networks

8. Translating and Explaining with Encoding – decoding Networks

9. Selecting Relevant Inputs or Memories with the Mechanism of Attention

10. Predicting Times Sequences with Advanced RNN

11. Learning from the Environment with Reinforcement

12. Learning Features with Unsupervised Generative Networks

13. Extending Deep Learning with Theano

Index

Simple recurrent network

An RNN is a network applied at multiple time steps but with a major difference: a connection to the previous state of layers at previous time steps named hidden states :

This can be written in the following form:

An RNN can be unrolled as a feedforward network applied on the sequence as input and with shared parameters between different time steps.

Input and output's first dimension is time, while next dimensions are for the data dimension inside each step. As seen in the previous chapter, the value at a time step (a word or a character) can be represented either by an index (an integer, 0-dimensional) or a one-hot-encoding vector (1-dimensional). The former representation is more compact in memory. In this case, input and output sequences will be 1-dimensional represented by a vector, with one dimension, the time:

x = T.ivector()
y = T.ivector()

The structure of the training program remains the same as in Chapter 2, Classifying Handwritten Digits with a Feedforward...

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Authors (1)

Bourez

Bourez

Christopher Bourez graduated from Ecole Polytechnique and Ecole Normale Suprieure de Cachan in Paris in 2005 with a Master of Science in Math, Machine Learning and Computer Vision (MVA). For 7 years, he led a company in computer vision that launched Pixee, a visual recognition application for iPhone in 2007, with the major movie theater brand, the city of Paris and the major ticket broker: with a snap of a picture, the user could get information about events, products, and access to purchase. While working on missions in computer vision with Caffe, TensorFlow or Torch, he helped other developers succeed by writing on a blog on computer science. One of his blog posts, a tutorial on the Caffe deep learning technology, has become the most successful tutorial on the web after the official Caffe website. On the initiative of Packt Publishing, the same recipes that made the success of his Caffe tutorial have been ported to write this book on Theano technology. In the meantime, a wide range of problems for Deep Learning are studied to gain more practice with Theano and its application.

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