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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

Vectorizing text

Machine learning models, including deep neural networks, take numeric information in and produce numeric output. The challenge with natural language processing then becomes, naturally, converting words to numbers.

There are a variety of ways that we can convert words to numbers. All of these methods satisfy the same goal, converting some sequence of words into a numeric vector. Some methods work better than others because, sometimes, when we make this conversion, we can lose some meaning in the translation.

NLP terminology

Let's start with by defining a few common terms, so that we remove any ambiguity their use might cause. I know that, since you can read, you likely have some understanding of these...

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