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Deep Learning Quick Reference

You're reading from  Deep Learning Quick Reference

Product type Book
Published in Mar 2018
Publisher Packt
ISBN-13 9781788837996
Pages 272 pages
Edition 1st Edition
Languages
Author (1):
Mike Bernico Mike Bernico
Profile icon Mike Bernico

Table of Contents (15) Chapters

Preface The Building Blocks of Deep Learning Using Deep Learning to Solve Regression Problems Monitoring Network Training Using TensorBoard Using Deep Learning to Solve Binary Classification Problems Using Keras to Solve Multiclass Classification Problems Hyperparameter Optimization Training a CNN from Scratch Transfer Learning with Pretrained CNNs Training an RNN from scratch Training LSTMs with Word Embeddings from Scratch Training Seq2Seq Models Using Deep Reinforcement Learning Generative Adversarial Networks 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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