Key Features

• Enter the world of AI with the help of solid concepts and real-world use cases
• Explore AI components to build real-world automated intelligence
• Become well versed with machine learning and deep learning concepts

Book Description

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.

What you will learn

• Use TensorFlow packages to create AI systems
• Build feedforward, convolutional, and recurrent neural networks
• Implement generative models for text generation
• Build reinforcement learning algorithms to play games
• Assemble RNNs, CNNs, and decoders to create an intelligent assistant
• Utilize RNNs to predict stock market behavior
• Create and scale training pipelines and deployment architectures for AI systems

Who this book is for

This book is designed for beginners in AI, aspiring AI developers, as well as machine learning enthusiasts with an interest in leveraging various algorithms to build powerful AI applications.

Table of Contents

1. The History of AI
2. Machine Learning Basics
3. Platforms and Other Essentials
4. Your First Artificial Neural Networks
5. Convolutional Neural Networks
6. Recurrent Neural Networks
7. Generative Models
8. Reinforcement Learning
9. Deep Learning for Intelligent Agents
10. Deep Learning for Game Playing
11. Deep Learning for Finance
12. Deep Learning for Robotics
13. Deploying and Maintaining AI Applications

About the Author

Patrick D. Smith is the Data Science Lead for Excella in Arlington, Virginia, where he founded the data science and machine learning team. Prior to Excella, Patrick was the lead instructor for the data science program at General Assembly in Washington, DC, as well as a data scientist with Booz Allen Hamilton's Strategic Innovations Group. He holds a bachelor's degree from The George Washington University in International Economics, and is currently a part-time masters student in software engineering at Harvard University.