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Hands-On Artificial Intelligence for Beginners

More Information
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
About

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.

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
Page Count 362
Course Length 10 hours 51 minutes
ISBN 9781788991063
Date Of Publication 31 Oct 2018
The beginnings of AI –1950–1974
Rebirth –1980–1987
The modern era takes hold – 1997-2005
Deep learning and the future – 2012-Present
Summary

Authors

Patrick D. Smith

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.