Book Image

TensorFlow Machine Learning Projects

By : Ankit Jain, Amita Kapoor
Book Image

TensorFlow Machine Learning Projects

By: Ankit Jain, Amita Kapoor

Overview of this book

TensorFlow has transformed the way machine learning is perceived. TensorFlow Machine Learning Projects teaches you how to exploit the benefits—simplicity, efficiency, and flexibility—of using TensorFlow in various real-world projects. With the help of this book, you’ll not only learn how to build advanced projects using different datasets but also be able to tackle common challenges using a range of libraries from the TensorFlow ecosystem. To start with, you’ll get to grips with using TensorFlow for machine learning projects; you’ll explore a wide range of projects using TensorForest and TensorBoard for detecting exoplanets, TensorFlow.js for sentiment analysis, and TensorFlow Lite for digit classification. As you make your way through the book, you’ll build projects in various real-world domains, incorporating natural language processing (NLP), the Gaussian process, autoencoders, recommender systems, and Bayesian neural networks, along with trending areas such as Generative Adversarial Networks (GANs), capsule networks, and reinforcement learning. You’ll learn how to use the TensorFlow on Spark API and GPU-accelerated computing with TensorFlow to detect objects, followed by how to train and develop a recurrent neural network (RNN) model to generate book scripts. By the end of this book, you’ll have gained the required expertise to build full-fledged machine learning projects at work.
Table of Contents (23 chapters)
Title Page
Copyright and Credits
Dedication
About Packt
Contributors
Preface
Index

Chapter 13. Generating Book Scripts Using LSTMs

Natural language generation (NLG), which is a sub-field of artificial intelligence, is a natural language processing task of generating human-readable text from various data inputs. It is an active area of research that has achieved great popularity in recent times.

The ability to generate natural language through machines can have wide variety of applications, including text autocomplete feature in phones, generating the summary of a document, and even generating new scripts for comedies. Google's Smart Reply also uses a technology that runs on similar lines to give reply suggestions when you're writing an email.

In this chapter, we will look at an NLG task of generating a book script from another Packt book that goes by the name of Mastering PostgreSQL 10. We took almost 100 pages of this book and removed any figures, tables, and SQL code. The data is fairly large and has enough words for a neural network to learn the nuances of the dataset...