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

Summary


Deep learning models provide better performance when the training dataset is large (big data). Training models for big data is computationally expensive. This problem can be handled using the divide and conquer approach: we divide the extensive computation part to many machines in a cluster, in other words, distributed AI.

One way of achieving this is by using Google's distributed TensorFlow, the API that helps in distributing the model training among different worker machines in the cluster. You need to specify the address of each worker machine and the parameter server. This makes the task of scaling the model difficult and cumbersome.

This problem can be solved by using the TensorFlowOnSpark API. By making minimal changes to the preexisting TensorFlow code, we can make it run on the cluster. The Spark framework handles the distribution among executor machines and the master, shielding the user from the details and giving better scalability.

In this chapter, the TensorFlowOnSpark...