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Deep Learning Quick Reference

You're reading from   Deep Learning Quick Reference Useful hacks for training and optimizing deep neural networks with TensorFlow and Keras

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Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781788837996
Length 272 pages
Edition 1st Edition
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Author (1):
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Mike Bernico Mike Bernico
Author Profile Icon Mike Bernico
Mike Bernico
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Table of Contents (15) Chapters Close

Preface 1. The Building Blocks of Deep Learning FREE CHAPTER 2. Using Deep Learning to Solve Regression Problems 3. Monitoring Network Training Using TensorBoard 4. Using Deep Learning to Solve Binary Classification Problems 5. Using Keras to Solve Multiclass Classification Problems 6. Hyperparameter Optimization 7. Training a CNN from Scratch 8. Transfer Learning with Pretrained CNNs 9. Training an RNN from scratch 10. Training LSTMs with Word Embeddings from Scratch 11. Training Seq2Seq Models 12. Using Deep Reinforcement Learning 13. Generative Adversarial Networks 14. Other Books You May Enjoy

Word embedding

Bag of Word models have a few less than ideal properties that are worth noting.

The first problem with the Bag of Word models we've previously looked at is that they don't consider the context of the word. They don't really consider the relationships that exist between the words in the document.

A second but related concern is that the assignment of words in the vector space is somewhat arbitrary. Information that might exist about the relation between two words in a corpus vocabulary might not be captured. For example, a model that has learned to process the word alligator can leverage very little of that learning when it comes across the word crocodile, even though both alligators and crocodiles are somewhat similar creatures that share many characteristics (bring on the herpetologist hate mail).

Lastly, because the vocabulary of a corpus can be...

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