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Natural Language Processing with TensorFlow

You're reading from   Natural Language Processing with TensorFlow Teach language to machines using Python's deep learning library

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Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788478311
Length 472 pages
Edition 1st Edition
Languages
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Authors (2):
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Thushan Ganegedara Thushan Ganegedara
Author Profile Icon Thushan Ganegedara
Thushan Ganegedara
Motaz Saad Motaz Saad
Author Profile Icon Motaz Saad
Motaz Saad
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Table of Contents (14) Chapters Close

Preface 1. Introduction to Natural Language Processing 2. Understanding TensorFlow FREE CHAPTER 3. Word2vec – Learning Word Embeddings 4. Advanced Word2vec 5. Sentence Classification with Convolutional Neural Networks 6. Recurrent Neural Networks 7. Long Short-Term Memory Networks 8. Applications of LSTM – Generating Text 9. Applications of LSTM – Image Caption Generation 10. Sequence-to-Sequence Learning – Neural Machine Translation 11. Current Trends and the Future of Natural Language Processing A. Mathematical Foundations and Advanced TensorFlow Index

Introducing Convolution Neural Networks

In this section, you will learn about CNNs. Specifically, you will first get an understanding of the sort of operations present in a CNN, such as convolution layers, pooling layers, and fully connected layers. Next, we will briefly see how all of these are connected to form an end-to-end model. Then we will dive into the details of each of these operations, define them mathematically, and learn how the various hyperparameters involved with these operations change the output produced by them.

CNN fundamentals

Now, let's explore the fundamental idea behind a CNN without delving into too much technical detail. As noted in the preceding paragraph, a CNN is a stack of layers, such as convolution layers, pooling layers, and fully connected layers. We will discuss each of these to understand their role in the CNN.

Initially, the input is connected to a set of convolution layers. These convolution layers slide a patch of weights (sometimes called the convolution...

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