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 15. What is Next?

Congratulations on making it this far. So far, have learned to implement a variety of cutting-edge AI algorithms in TensorFlow and built cool projects on the side. Specifically, we have built projects on reinforcement learning, Bayesian neural networks, capsule networks, and Generative Adversarial Networks (GANs), among others. We have also learned about several modules of TensorFlow, including TensorFlow.js, TensorFlow Lite, and TensorFlow Probability, among others. This surely deserves a pat on the back and a well-earned rest.

Before we go out to play, there are a few more things that we should consider reading about before we are prime-time ready to deploy these cutting edge techniques in production. As we will realize in this chapter, there is more to deploying a machine learning model in production than just implementing the latest research paper in AI. To understand what I mean by this, let's read through the following topics:

  • TensorFlow utilities to deploy...