Book Image

Hands-On Artificial Intelligence for Beginners

By : Patrick D. Smith, David Dindi
Book Image

Hands-On Artificial Intelligence for Beginners

By: Patrick D. Smith, David Dindi

Overview of this book

Virtual Assistants, such as Alexa and Siri, process our requests, Google's cars have started to read addresses, and Amazon's prices and Netflix's recommended videos are decided by AI. Artificial Intelligence is one of the most exciting technologies and is becoming increasingly significant in the modern world. Hands-On Artificial Intelligence for Beginners will teach you what Artificial Intelligence is and how to design and build intelligent applications. This book will teach you to harness packages such as TensorFlow in order to create powerful AI systems. You will begin with reviewing the recent changes in AI and learning how artificial neural networks (ANNs) have enabled more intelligent AI. You'll explore feedforward, recurrent, convolutional, and generative neural networks (FFNNs, RNNs, CNNs, and GNNs), as well as reinforcement learning methods. In the concluding chapters, you'll learn how to implement these methods for a variety of tasks, such as generating text for chatbots, and playing board and video games. By the end of this book, you will be able to understand exactly what you need to consider when optimizing ANNs and how to deploy and maintain AI applications.
Table of Contents (15 chapters)

Reinforcement Learning

Along with generative networks, reinforcement learning algorithms have provided the most visible advances in Artificial Intelligence (AI) today. For many years, computer scientists have worked toward creating algorithms and machines that can perceive and react to their environment like a human would. Reinforcement learning is a manifestation of that, giving us the wildly popular AlphaGo and self-driving cars. In this chapter, we'll cover the foundations of reinforcement learning that will allow us to create advanced artificial agents later in this book.

Reinforcement learning plays off the human notion of learning from experience. Like generative models, it learns based on evaluative feedback. Unlike instructive feedback in supervised learning where the network learns by us telling it how to do something, evaluative feedback helps algorithms learn...