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)

Summary

In this chapter, we learned about some of the most exciting networks in AI, variational autoencoders and GANs. Each of these relies on the same fundamental concepts of condensing data, and then generating from again from that condensed form of data. You will recall that both of these networks are probabilistic models, meaning that they rely on inference from probability distributions in order to generate data. We worked through examples of both of these networks, and showed how we can use them to generate new images.

In addition to learning about these exciting new techniques, most importantly you learned that the building blocks of advanced networks can be broken down into smaller, simpler, and repetitive parts. When you think about writing advanced models in TensorFlow, you need to remember what kind of layers you need, what type of activation functions you need, how...