Book Image

Artificial Intelligence By Example - Second Edition

By : Denis Rothman
Book Image

Artificial Intelligence By Example - Second Edition

By: Denis Rothman

Overview of this book

AI has the potential to replicate humans in every field. Artificial Intelligence By Example, Second Edition serves as a starting point for you to understand how AI is built, with the help of intriguing and exciting examples. This book will make you an adaptive thinker and help you apply concepts to real-world scenarios. Using some of the most interesting AI examples, right from computer programs such as a simple chess engine to cognitive chatbots, you will learn how to tackle the machine you are competing with. You will study some of the most advanced machine learning models, understand how to apply AI to blockchain and Internet of Things (IoT), and develop emotional quotient in chatbots using neural networks such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs). This edition also has new examples for hybrid neural networks, combining reinforcement learning (RL) and deep learning (DL), chained algorithms, combining unsupervised learning with decision trees, random forests, combining DL and genetic algorithms, conversational user interfaces (CUI) for chatbots, neuromorphic computing, and quantum computing. By the end of this book, you will understand the fundamentals of AI and have worked through a number of examples that will help you develop your AI solutions.
Table of Contents (23 chapters)
21
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22
Index

Summary

Applying artificial intelligence to Amazon's real-time sales, production, and delivery forces projects into reality.

Learning machine learning and deep learning with MNIST, CIFAR, and other ready-to-use datasets with ready-to-use programs is a prerequisite to mastering artificial intelligence. Learning mathematics is a must.

Building a few programs that can do various theoretical things cannot be avoided. However, managing a real project under corporate pressure will bring an AI specialist up to another level. The AI specialist will have to put AI theory into practice. The constraints of corporate specifications make machine learning projects exciting. During those projects, experts learn valuable information on how AI solutions work and can be improved.

This chapter described an RL-DL-CRLMM model with an optimizer. We learned how the market is evolving from planning manufacturing in advance to real-time planning challenging classical processes. We saw...