Book Image

Natural Language Processing with TensorFlow - Second Edition

By : Thushan Ganegedara
2 (1)
Book Image

Natural Language Processing with TensorFlow - Second Edition

2 (1)
By: Thushan Ganegedara

Overview of this book

Learning how to solve natural language processing (NLP) problems is an important skill to master due to the explosive growth of data combined with the demand for machine learning solutions in production. Natural Language Processing with TensorFlow, Second Edition, will teach you how to solve common real-world NLP problems with a variety of deep learning model architectures. The book starts by getting readers familiar with NLP and the basics of TensorFlow. Then, it gradually teaches you different facets of TensorFlow 2.x. In the following chapters, you then learn how to generate powerful word vectors, classify text, generate new text, and generate image captions, among other exciting use-cases of real-world NLP. TensorFlow has evolved to be an ecosystem that supports a machine learning workflow through ingesting and transforming data, building models, monitoring, and productionization. We will then read text directly from files and perform the required transformations through a TensorFlow data pipeline. We will also see how to use a versatile visualization tool known as TensorBoard to visualize our models. By the end of this NLP book, you will be comfortable with using TensorFlow to build deep learning models with many different architectures, and efficiently ingest data using TensorFlow Additionally, you’ll be able to confidently use TensorFlow throughout your machine learning workflow.
Table of Contents (15 chapters)
12
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13
Index

Index

Symbols

1-gram precision 354

A

Adam optimizer 13

additive attention layer

reference link 344

Amazon Web Services (AWS)

URL 21

Anaconda

download link 18

installation link 18

installing 18

Application Programming Interface (API) 24

attention patterns

visualizing 355, 357, 360

AutoGraph 26

Automatic Language Processing Advisory Committee (ALPAC) 314

average pooling 157

B

backpropagation (BP) 197

avoiding, for RNNs 199

working 197, 198

Backpropagation Through Time (BPTT) 197, 256

limitations 201, 202

RNNs, training 200

bag-of-words 6, 8

batch normalization 376

Bayes' rule 461, 462

beam 260

beam length 260

beam search 260, 301, 302

implementing 302, 304

text, generating with 304, 305

used, for improving LSTMs 260, 261

bell curve 459

Bidirectional Encoder Representation from Transformers (BERT) 377, 378

answering questions, with 399,...