Natural Language Processing with TensorFlow

From introductory NLP tasks to Transformer models, this new edition teaches you to utilize powerful TensorFlow APIs to implement end-to-end NLP solutions driven by performant ML (Machine Learning) models

Key Features

• Learn to solve common NLP problems effectively with TensorFlow 2.x
• Implement end-to-end data pipelines guided by the underlying ML model architecture
• Use advanced LSTM techniques for complex data transformations, custom models and metrics

Book Description

Learning to solve natural language processing 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 to solve common real-world NLP problems with a variety of deep learning model architectures.

The book starts with getting the readers familiar with NLP and basics of TensorFlow. Then it gradually teaches different facets of TensorFlow 2.x. In the following chapters, you then learn to generate powerful word vectors, classify text, generate new text and generate image captions, among other exciting use-cases of real-world natural language processing.

TensorFlow has evolved to be an ecosystem that supports machine learning workflow through ingesting and transforming data, building models, monitoring and tracking. 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 using TensorFlow for building deep learning models with many different architectures. Additionally, you'll confidently use TensorFlow throughout your machine learning workflow.

What you will learn

• Core concepts NLP and techniques with TensorFlow
• State of the art Transformers and how they are used to solve NLP tasks
• Sentence classification and text generation using CNNs and RNNS
• Advanced models for machine translation and image caption generation
• Building end-to-end data pipelines in TensorFlow
• Interesting facts and practices related to the task at hand
• Word representations of large amounts of data for deep learning

Who This Book Is For

This book is for Python developers and programmers with a strong interest in deep learning, who want to learn how to leverage TensorFlow to simplify NLP tasks.

Fundamental Python skills are assumed, as well as basic knowledge of machine learning and undergraduate-level calculus and linear algebra. No previous natural language processing experience required.

Table of Contents

1. Introduction to Natural Language Processing
2. Understanding TensorFlow
3. Word2vec-Learning Word Embeddings, begins by discussing how to solve NLP tasks with TensorFlow2.x
4. Advanced word2vec
5. Sentence Classification with CNNs
6. Recurrent Neural Networks
7. Long Short Term Memory Networks (LSTMs)
8. Application of LSTM - Generating Text
9. Application of LSTM - Image Caption Generation
10. Sequence to Sequence - Machine Translation
11. Sequence to Sequence-Transformers
12. Appendix