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 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.

What you will learn
Learn core concepts of NLP and techniques with TensorFlow
Use state-of-the-art Transformers and how they are used to solve NLP tasks
Perform sentence classification and text generation using CNNs and RNNs
Utilize advanced models for machine translation and image caption generation
Build end-to-end data pipelines in TensorFlow
Learn interesting facts and practices related to the task at hand
Create 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
Introduction to Natural Language Processing
Understanding TensorFlow 2
Word2vec – Learning Word Embeddings
Advanced Word Vector Algorithms
Sentence Classification with Convolutional Neural Networks
Recurrent Neural Networks
Understanding Long Short-Term Memory Networks
Applications of LSTM – Generating Text
Sequence-to-Sequence Learning – Neural Machine Translation
Transformers
Image Captioning with Transformers
Appendix A: Mathematical Foundations and Advanced TensorFlow