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Deep Learning with Theano

You're reading from  Deep Learning with Theano

Product type Book
Published in Jul 2017
Publisher Packt
ISBN-13 9781786465825
Pages 300 pages
Edition 1st Edition
Languages
Author (1):
Christopher Bourez Christopher Bourez
Profile icon Christopher Bourez

Table of Contents (22) Chapters

Deep Learning with Theano
Credits
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
1. Theano Basics 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

A dataset for natural language


As a dataset, any text corpus can be used, such as Wikipedia, web articles, or even with symbols such as code or computer programs, theater plays, and poems; the model will catch and reproduce the different patterns in the data.

In this case, let's use tiny Shakespeare texts to predict new Shakespeare texts or at least, new texts written in a style inspired by Shakespeare; two levels of predictions are possible, but can be handled in the same way:

  • At the character level: Characters belong to an alphabet that includes punctuation, and given the first few characters, the model predicts the next characters from an alphabet, including spaces to build words and sentences. There is no constraint for the predicted word to belong to a dictionary and the objective of training is to build words and sentences close to real ones.

  • At the word level: Words belong to a dictionary that includes punctuation, and given the first few words, the model predicts the next word out...

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