Natural Language Processing with TensorFlow: The definitive NLP book to implement the most sought-after machine learning models and tasks, 2nd Edition

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

1. Introduction to Natural Language Processing
2. Understanding TensorFlow 2
3. Word2vec – Learning Word Embeddings
4. Advanced Word Vector Algorithms
5. Sentence Classification with Convolutional Neural Networks
6. Recurrent Neural Networks
7. Understanding Long Short-Term Memory Networks
8. Applications of LSTM – Generating Text
9. Sequence-to-Sequence Learning – Neural Machine Translation
10. Transformers
11. Image Captioning with Transformers
12. Appendix A: Mathematical Foundations and Advanced TensorFlow

Thushan is a seasoned ML practitioner with 4+ years of experience in the industry. Currently he is a senior machine learning engineer at Canva; an Australian startup that founded the online visual design software, Canva, serving millions of customers. His efforts are particularly concentrated in the search and recommendations group working on both visual and textual content. Prior to Canva, Thushan was a senior data scientist at QBE Insurance; an Australian Insurance company. Thushan was developing ML solutions for use-cases related to insurance claims. He also led efforts in developing a Speech2Text pipeline there. He obtained his PhD specializing in machine learning from the University of Sydney in 2018.