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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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Toc

Table of Contents (14) Chapters Close

Preface 1. Introduction to Natural Language Processing FREE CHAPTER 2. Understanding TensorFlow 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

Implementing our first neural network

Great! Now that you've learned the architecture, basics, and scoping mechanism of TensorFlow, it's high time that we move on and implement something moderately complex. Let's implement a neural network. Precisely, we will implement a fully connected neural network model that we discussed in Chapter 1, Introduction to Natural Language Processing.

One of the stepping stones to the introduction of neural networks is to implement a neural network that is able to classify digits. For this task, we will be using the famous MNIST dataset made available at http://yann.lecun.com/exdb/mnist/. You might feel a bit skeptical regarding our using a computer vision task rather than a NLP task. However, vision tasks can be implemented with less preprocessing and are easy to understand.

As this is our first encounter with neural networks, we will walk through the main parts of the example. However, note that I will only walk through the crucial bits of...

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