natural_language_processing_with_pytorch.pdf

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Build Intelligent Language Applications Using
Deep Learning
Natural Language
Processing with PyTorch
Delip Rao and Brian McMahan
Beijing
Boston Farnham Sebastopol
Tokyo
Natural Language Processing with PyTorch
by Delip Rao and Brian McMahan
Copyright © 2019 Delip Rao and Brian McMahan. All rights reserved.
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February 2019:
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2019-01-16:
First Release
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Natural Language Processing with
PyTorch,
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thereof complies with such licenses and/or rights.
978-1-491-97823-8
[LSI]
Table of Contents
Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
1.
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
The Supervised Learning Paradigm
Observation and Target Encoding
One-Hot Representation
TF Representation
TF-IDF Representation
Target Encoding
Computational Graphs
PyTorch Basics
Installing PyTorch
Creating Tensors
Tensor Types and Size
Tensor Operations
Indexing, Slicing, and Joining
Tensors and Computational Graphs
CUDA Tensors
Exercises
Solutions
Summary
References
2
5
6
7
8
9
10
11
13
13
16
17
19
22
24
26
26
27
27
29
32
33
34
iii
2.
A Quick Tour of Traditional NLP. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
Corpora, Tokens, and Types
Unigrams, Bigrams, Trigrams, …, N-grams
Lemmas and Stems
Categorizing Sentences and Documents

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