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The Deep Learning Workshop

You're reading from   The Deep Learning Workshop Learn the skills you need to develop your own next-generation deep learning models with TensorFlow and Keras

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Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781839219856
Length 474 pages
Edition 1st Edition
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Authors (5):
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Nipun Sadvilkar Nipun Sadvilkar
Author Profile Icon Nipun Sadvilkar
Nipun Sadvilkar
Thomas Joseph Thomas Joseph
Author Profile Icon Thomas Joseph
Thomas Joseph
Anthony So Anthony So
Author Profile Icon Anthony So
Anthony So
Mohan Kumar Silaparasetty Mohan Kumar Silaparasetty
Author Profile Icon Mohan Kumar Silaparasetty
Mohan Kumar Silaparasetty
Mirza Rahim Baig Mirza Rahim Baig
Author Profile Icon Mirza Rahim Baig
Mirza Rahim Baig
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Toc

Table of Contents (9) Chapters Close

Preface
1. Building Blocks of Deep Learning 2. Neural Networks FREE CHAPTER 3. Image Classification with Convolutional Neural Networks (CNNs) 4. Deep Learning for Text – Embeddings 5. Deep Learning for Sequences 6. LSTMs, GRUs, and Advanced RNNs 7. Generative Adversarial Networks Appendix

Sequence Models for Text Classification

In Chapter 5, Deep Learning for Sequences, we learned that RNNs perform extremely well on sequence-modeling tasks and provide high performance on text-related tasks. In this chapter, we will use plain RNNs and variants of RNNs on a sentiment classification task: processing the input sequence and predicting whether the sentiment is positive or negative.

We'll use the IMDb reviews dataset for this task. The dataset contains 50,000 movie reviews, along with their sentiment – 25,000 highly polar movie reviews for training and 25,000 for testing. A few reasons for using this dataset are as follows:

  • It is very conveniently available to load Keras (tokenized version) with a single command.
  • The dataset is commonly used for testing new approaches/models. This should help you compare your results with other approaches easily.
  • Longer sequences in the data (IMDb reviews can get very long) help us assess the differences between...
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