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Deep Learning with TensorFlow 2 and Keras

You're reading from   Deep Learning with TensorFlow 2 and Keras Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API

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Product type Paperback
Published in Dec 2019
Publisher Packt
ISBN-13 9781838823412
Length 646 pages
Edition 2nd Edition
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Authors (3):
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Dr. Amita Kapoor Dr. Amita Kapoor
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Dr. Amita Kapoor
Sujit Pal Sujit Pal
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Sujit Pal
Antonio Gulli Antonio Gulli
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Antonio Gulli
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Table of Contents (19) Chapters Close

Preface 1. Neural Network Foundations with TensorFlow 2.0 2. TensorFlow 1.x and 2.x FREE CHAPTER 3. Regression 4. Convolutional Neural Networks 5. Advanced Convolutional Neural Networks 6. Generative Adversarial Networks 7. Word Embeddings 8. Recurrent Neural Networks 9. Autoencoders 10. Unsupervised Learning 11. Reinforcement Learning 12. TensorFlow and Cloud 13. TensorFlow for Mobile and IoT and TensorFlow.js 14. An introduction to AutoML 15. The Math Behind Deep Learning 16. Tensor Processing Unit 17. Other Books You May Enjoy
18. Index

Sentence and paragraph embeddings

A simple, yet surprisingly effective solution for generating useful sentence and paragraph embeddings is to average the word vectors of their constituent words. Even though we will describe some popular sentence and paragraph embeddings in this section, it is generally always advisable to try averaging the word vectors as a baseline.

Sentence (and paragraph) embeddings can also be created in a task optimized way by treating them as a sequence of words, and representing each word using some standard word vector. The sequence of word vectors is used as input to train a network for some task. Vectors extracted from one of the later layers of the network just before the classification layer generally tend to produce very good vector representation for the sequence. However, they tend to be very task specific, and are of limited use as a general vector representation.

An idea for generating general vector representations for sentences that could be...

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