"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