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Deep Learning with Theano

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

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781786465825
Length 300 pages
Edition 1st Edition
Tools
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Author (1):
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Christopher Bourez Christopher Bourez
Author Profile Icon Christopher Bourez
Christopher Bourez
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Table of Contents (15) Chapters Close

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

Summary

The attention mechanism is a smart option to help neural networks select the right information and focus to produce the correct output. It can be placed either directly on the inputs or the features (inputs processed by a few layers). Accuracies in the cases of translation, image annotation, and speech recognition, are increased, in particular when the dimension of the inputs is important.

Attention mechanism has led to new types of networks enhanced with external memory, working as an input/output, from which to read or to which to write. These networks have proved to be very powerful in question-answering challenges, into which most tasks in natural language processing can can be cast: tagging, classification, sequence-to-sequence, or question answering tasks.

In the next chapter, we'll see more advanced techniques and their application to the more general case of recurrent neural networks, to improve accuracy.

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