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


In this chapter, we learned what reinforcement learning is. Reinforcement learning is an advanced technique that you will find is often used to solve complex problems. We learned about OpenAI Gym, a framework that provides an environment for simulating many popular games in order to implement and practice reinforcement learning algorithms. We touched on deep reinforcement learning concepts, and we encourage you to explore books (mentioned in the further reading) specifically written about reinforcement learning to learn deeply about the theories and concepts.

We learned how to play the PacMan game in OpenAI Gym. We implemented DQN and used it to learn to play the PacMan game. We only used an MLP network to keep things simple, but, for complex examples, you may end up using complex CNN, RNN, or Sequence-to-Sequence models.

In the next chapter, we shall learn about future opportunities in the fields of machine learning and TensorFlow.