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"This book presents recent and upcoming innovations in artificial intelligence in an approachable and friendly way. The breadth of topics covered in the book is staggering, ranging from traditional methods like reinforcement learning and K-means clustering all the way to neuromorphic and quantum computing. If you want to be exposed to what AI researchers are working on today from a practitioner's perspective, I cannot recommend this book enough."

- Trevor Bekolay, Co-founder of Applied Brain Research, Co-author of Neural Modeling of Speech Processing and Speech Learning: An Introduction

"If you want a practical understanding of Artificial Intelligence, I recommend reading Denis Rothman's recent book Artificial Intelligence By Example, Second Edition. He's an excellent writer with practical, real-world experience, capable of teaching a wide range of AI algorithms."

- Adrian Rosebrock, Chief PyImageSearcher, PyImageSearch

"There aren't many books - especially in tech - that cross the 500-page mark and keep you as captivated as this one. The second edition of Denis Rothman's Artificial Intelligence By Example is a nice and easily digestible amalgam of the fundamentals of deep learning and intuitive examples that help you learn and use them in the real world. Rothman ends with a series of informative chapters about neuromorphic and quantum computing - a new field that is bound to keep researchers, chip manufacturers, and the overall technology enthusiast glued to what's next in the coming decade."

- Tarry Singh, Founder & CEO of deepkapha.ai, curae.ai, and Real AI Inc.

Preface

Chapter 1: Getting Started with Next-Generation Artificial Intelligence through Reinforcement Learning
Chapter 2: Building a Reward Matrix - Designing Your Datasets
Chapter 3: Machine Intelligence - Evaluation Functions and Numerical Convergence
Chapter 4: Optimizing Your Solutions with K-Means Clustering
Chapter 5: How to Use Decision Trees to Enhance K-Means Clustering
Chapter 6: Innovating AI with Google Translate
Chapter 7: Optimizing Blockchains with Naive Bayes
Chapter 8: Solving the XOR Problem with a FNN
Chapter 9: Abstract Image Classification with CNN
Chapter 10: Conceptual Representation Learning
Chapter 11: Combining RL and DL
Chapter 12: AI and the IoT
Chapter 13: Visualizing Networks with TensorFlow 2.x and TensorBoard
Chapter 14: Preparing the Input of Chatbots with RBMs and PCA
Chapter 15: Setting Up a Cognitive NLP UI/CUI Chatbot
Chapter 16: Improving the Emotional Intelligence Deficiencies of Chatbots
Chapter 17: Genetic Algorithms in Hybrid Neural Networks
Chapter 18: Neuromorphic Computing
Chapter 19: Quantum Computing

Appendix: Answers to the Questions
Other Books You May Enjoy
Index

Understand the fundamentals and develop your own AI solutions in this updated edition packed with many new examples

Key Features
AI-based examples to guide you in designing and implementing machine intelligence
Build machine intelligence from scratch using artificial intelligence examples
Develop machine intelligence from scratch using real artificial intelligence
Book Description
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.

What you will learn
Apply k-nearest neighbors (KNN) to language translations and explore the opportunities in Google Translate
Understand chained algorithms combining unsupervised learning with decision trees
Solve the XOR problem with feedforward neural networks (FNN) and build its architecture to represent a data flow graph
Learn about meta learning models with hybrid neural networks
Create a chatbot and optimize its emotional intelligence deficiencies with tools such as Small Talk and data logging
Building conversational user interfaces (CUI) for chatbots
Writing genetic algorithms that optimize deep learning neural networks
Build quantum computing circuits
Who this book is for
Developers and those interested in AI, who want to understand the fundamentals of Artificial Intelligence and implement them practically. Prior experience with Python programming and statistical knowledge is essential to make the most out of this book.