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Deep Learning with TensorFlow and Keras – 3rd edition

You're reading from   Deep Learning with TensorFlow and Keras – 3rd edition Build and deploy supervised, unsupervised, deep, and reinforcement learning models

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Product type Paperback
Published in Oct 2022
Publisher Packt
ISBN-13 9781803232911
Length 698 pages
Edition 3rd Edition
Tools
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Authors (3):
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Sujit Pal Sujit Pal
Author Profile Icon Sujit Pal
Sujit Pal
Antonio Gulli Antonio Gulli
Author Profile Icon Antonio Gulli
Antonio Gulli
Dr. Amita Kapoor Dr. Amita Kapoor
Author Profile Icon Dr. Amita Kapoor
Dr. Amita Kapoor
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Table of Contents (23) Chapters Close

Preface 1. Neural Network Foundations with TF 2. Regression and Classification FREE CHAPTER 3. Convolutional Neural Networks 4. Word Embeddings 5. Recurrent Neural Networks 6. Transformers 7. Unsupervised Learning 8. Autoencoders 9. Generative Models 10. Self-Supervised Learning 11. Reinforcement Learning 12. Probabilistic TensorFlow 13. An Introduction to AutoML 14. The Math Behind Deep Learning 15. Tensor Processing Unit 16. Other Useful Deep Learning Libraries 17. Graph Neural Networks 18. Machine Learning Best Practices 19. TensorFlow 2 Ecosystem 20. Advanced Convolutional Neural Networks 21. Other Books You May Enjoy
22. Index

Sentiment analysis

What is the code we used to test Colab? It is an example of sentiment analysis developed on top of the IMDB dataset. The IMDB dataset contains the text of 50,000 movie reviews from the Internet Movie Database. Each review is either positive or negative (for example, thumbs up or thumbs down). The dataset is split into 25,000 reviews for training and 25,000 reviews for testing. Our goal is to build a classifier that can predict the binary judgment given the text. We can easily load IMDB via tf.keras and the sequences of words in the reviews have been converted to sequences of integers, where each integer represents a specific word in a dictionary. We also have a convenient way of padding sentences to max_len, so that we can use all sentences, whether short or long, as inputs to a neural network with an input vector of fixed size:

import tensorflow as tf
from tensorflow.keras import datasets, layers, models, preprocessing
import tensorflow_datasets as tfds
max_len...
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