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)

Platforms and Other Essentials

In this chapter, we'll discuss important libraries and frameworks that one needs to get started in Artificial Intelligence (AI). We'll cover the basic functions of the three most popular deep learning frameworks—TensorFlow, PyTorch, and Keras—show you how to get up and running in each of these frameworks, as we will be utilizing them in the following chapters. We'll touch upon computing for AI, and discuss how GPUs and other advanced memory units can improve it. Lastly, we'll discuss the fundamentals of two popular cloud computing frameworks for deep learning: AWS and Google Cloud.

The following topics will be covered in this chapter:

  • Essential libraries for deep learning in Python: TensorFlow, PyTorch, and Keras
  • CPUs, GPUs, and compute frameworks that are used for AI
  • The fundamentals of AWS and Google Cloud
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