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)

Variational autoencoders

Variational autoencoders (VAEs) are built on the idea of the standard autoencoder, and are powerful generative models and one of the most popular means of learning a complicated distribution in an unsupervised fashion. VAEs are probabilistic models rooted in Bayesian inference. A probabilistic model is exactly as it sounds:

Probabilistic models incorporate random variables and probability distributions into the model of an event or phenomenon.

VAEs, and other generative models, are probabilistic in that they seek to learn a distribution that they utilize for subsequent sampling. While all generative models are probabilistic models, not all probabilistic models are generative models.

The probabilistic structure of VAEs comes into play with their encoders. Instead of building an encoder that outputs a single value to describe the input data, we want to learn...