Packt+ | Advance your knowledge in tech

0

All Products

Best Sellers

New Releases

Books

Videos

Audiobooks

Free Learning

Natural Language Processing with TensorFlow

You're reading from Natural Language Processing with TensorFlow Teach language to machines using Python's deep learning library

Product type Paperback

Published in May 2018

Publisher Packt

ISBN-13 9781788478311

Length 472 pages

Edition 1st Edition

Languages

Processing

Tools

Processing

Concepts

Deep Learning

Authors (2):

Saad

Ganegedara

View More author details

Table of Contents (16) Chapters

Natural Language Processing with TensorFlow

Contributors

Preface

1. Introduction to Natural Language Processing FREE CHAPTER

2. Understanding TensorFlow

3. Word2vec – Learning Word Embeddings

4. Advanced Word2vec

5. Sentence Classification with Convolutional Neural Networks

6. Recurrent Neural Networks

7. Long Short-Term Memory Networks

8. Applications of LSTM – Generating Text

9. Applications of LSTM – Image Caption Generation

10. Sequence-to-Sequence Learning – Neural Machine Translation

11. Current Trends and the Future of Natural Language Processing

Mathematical Foundations and Advanced TensorFlow

Index

Chapter 10. Sequence-to-Sequence Learning – Neural Machine Translation

Sequence-to-sequence learning is the term used for tasks that require mapping an arbitrary length sequence to another arbitrary length sequence. This is one of the most sophisticated tasks that involves learning many-to-many mappings. Examples of this task include Neural Machine Translation (NMT) and creating chatbots. NMT is where we translate a sentence from one language (source language) to another (target language). Google Translate is an example of an NMT system. Chatbots (that is, software that can communicate with/answer a person) are able to converse with humans in a realistic manner. This is especially useful for various service providers, as chatbots can be used to find answers for easily solvable questions which customers might have, instead of redirecting them to human operators.

In this chapter, we will learn how to implement a NMT system. However, before diving directly into such recent advances, we will...

The rest of the chapter is locked

Register for a free Packt account to unlock a world of extra content!

A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.

Unlock this book and the full library FREE for 7 days

Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of

Start free trial

Renews at €14.99/month. Cancel anytime

Authors (2)

Saad

Saad

See other products by Saad

Ganegedara

Ganegedara

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.

See other products by Ganegedara

Other recommended products

Related to this chapter

Getting Started with Google BERT

Getting Started with Google BERT

Getting Started with Google BERT will help you become well-versed with the BERT model from scratch and learn how to create interesting NLP applications. You'll understand several variants of BERT such as ALBERT, RoBERTa, DistilBERT, ELECTRA, VideoBERT, and many others in detail.

Jan 2021 11h 44m

Recurrent Neural Networks with Python Quick Start Guide

Recurrent Neural Networks with Python Quick Start Guide

Developers struggle to find an easy to follow learning resource for implementing Recurrent Neural Network(RNN) models. RNNs are the state-of-the-art model in deep learning for dealing with sequential data. From language translation to generating captions for an image, RNNs are used to continuously improve the results. This book will teach you the fundamentals of RNNs with example applications in Python and the TensorFlow library. The examples are accompanied by the right combination of theoretical knowledge and real-world implementations of concepts to build a solid foundation of neural network modeling.

Deep Learning Essentials

Deep Learning Essentials

Deep Learning is one of the trending topics in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning. This book will help you take your first steps when it comes to training efficient deep learning models, and apply them in various practical scenarios. You will model, train and deploy different kinds of neural networks such as Convolutional Neural Network, Recurrent Neural Network, and see their applications in real-world domains such as computer vision, natural language processing, and speech recognition. This book also covers solutions to tackle different problems you might come across while training your models and ensure their high performance. This book does not assume any prior knowledge of deep learning. By the end of this book, you will have a firm understanding of the basics of deep learning and neural network modeling, along with their practical applications.

Jan 2018 9h 28m

Hands-On Deep Learning Algorithms with Python

Hands-On Deep Learning Algorithms with Python

This book introduces basic-to-advanced deep learning algorithms used in a production environment by AI researchers and principal data scientists; it explains algorithms intuitively, including the underlying math, and shows how to implement them using popular Python-based deep learning libraries such as TensorFlow.

Jul 2019 17h 4m

Hands-On Natural Language Processing with PyTorch 1.x

Hands-On Natural Language Processing with PyTorch 1.x

Developers working with NLP will be able to put their knowledge to work with this practical guide to PyTorch. You will learn to use PyTorch offerings and how to understand and analyze text using Python. You will learn to extract the underlying meaning in the text using deep neural networks and modern deep learning algorithms.

Jul 2020 9h 12m

Advanced Natural Language Processing with TensorFlow 2

Advanced Natural Language Processing with TensorFlow 2

This book provides hands-on training in NLP tools and techniques with intrinsic details. Apart from gaining expertise, you will be able to carry out novel state-of-the-art research using the skills gained.

Feb 2021 12h 40m

Intelligent Projects Using Python

Intelligent Projects Using Python

This book includes 9 projects on building smart and practical AI-based systems. These projects cover solutions to different domain-specific problems in healthcare, e-commerce and more. With this book, you will apply different machine learning and deep learning techniques and learn how to build your own intelligent applications for smart predictions and other insight-driven tasks.

Jan 2019 11h 24m

Deep Learning with Theano

Deep Learning with Theano

This book covers a complete overview of Deep Learning with Theano, a Python-based library that makes optimizing numerical expressions easy. Practical code examples address supervised, unsupervised, generative and reinforcement learning for image recognition, natural language processing, or game strategy, with best performing nets and principles.

Jul 2017 10h 0m

Neural Networks with Keras Cookbook

Neural Networks with Keras Cookbook

This book presents solutions to the majority of the challenges you will face while training neural networks to solve deep learning problems. It covers the trending deep learning architectures used in industry and tackles a variety of use cases in computer vision, text processing, audio analysis, recommender systems, and game bots

Feb 2019 18h 56m

Hands-On Python Natural Language Processing

Hands-On Python Natural Language Processing

This book provides a blend of both the theoretical and practical aspects of Natural Language Processing (NLP). It covers the concepts essential to develop a thorough understanding of NLP and also delves into a detailed discussion on NLP based use-cases such as language translation, sentiment analysis, etc. Every module covers real-world examples

Jun 2020 10h 32m

Hands-On Natural Language Processing with Python

Hands-On Natural Language Processing with Python

This book teaches you to leverage deep learning models in performing various NLP tasks along with showcasing the best practices in dealing with the NLP challenges. The book equips you with practical knowledge to implement deep learning in your linguistic applications using NLTk and Python's popular deep learning library, TensorFlow.

Jul 2018 10h 24m

Deep Learning with Keras

Deep Learning with Keras

Keras is a high-level neural network library written in Python that runs on top of either Theano or TensorFlow. With this book, you'll learn the basics of Keras in a highly practical way and understand how this minimal, highly modular framework runs on both CPU and GPU, allowing you to put your ideas into action in the shortest possible time.

Apr 2017 10h 36m