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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

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

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Amazon verified reviews

user Apr 03, 2020

There is only one way to learn data science: With a solid understanding of the underlying theory, and by getting your hands dirty with real examples, data, and code. AI by example delivers exactly this mix, with examples that go beyond contrived toy boxes, and real code on git.While I wish the book had a chapter how to assure ML by scrutinizing data, debugging models, assessing model competence, and how to apply general software engineering principles as algorithms are derived from data, I understand that these issues concern a more experienced audience and would be out of scope for beginners. Still, I can see this book as a reference even for more experienced professionals, and it would be great to see these aspects considered in a third edition.That said, this book is equally for developers who need to learn how to build solid machine learning programs, project managers and consultants need to learn what ML is and is not capable of, as well as teachers and students who get a comprehensive overview of the key aspects of AI, along with their first hands-on experience training models.

Amazon Verified review

Prometheus Unbound May 21, 2020

This second addition of Artificial Intelligence by Example successfully builds on the readability and approachability of the first addition. The author does a very good job at guiding the reader through "how to think about AI" using many diverse examples. For each example, he explains the conceptual, mathematical and implementation viewpoints in an easy to understand manner. The author is a teacher who communicates through readable and accessible discourse. Like the first edition, this revised second addition of Artificial Intelligence by Example is a good book for those who want to get started in understanding and coding AI algorithms. And in case the reader wishes to venture beyond practical approaches to AI algorithms, Rothman does not disappoint with his discussions on cognitive neuromorphic architectures and quantum computers. I recommend the book to early career engineers who want to sharpen how they think about AI in practical and abstract ways.

SR_WA Dec 01, 2023

This is one of the best authored books on ai.The explanation flows well making it easy to understand.

michael mejia Mar 21, 2020

Too many books are only math oriented and miss the implementation of using such algorithms in programming languages. Other books focus on programming the concepts but don't go in depth on the math behind it. From start to finish this book gives you both worlds. No longer do i have this black mystery box mentality of implementing an algorithm(using a library) knowing how to use it but have no idea how it works behind the scenes. This book combines a mathematician, developer, and has Business logic. I Would highly recommend this book.

Alexandru Balasescu Mar 20, 2020

A couple of years ago I went to see Jimmy Cliff performing live. In a break between two songs he said: “Have you heard of SkaRock? Let me tell you about it; I was there at the beginning.” Jimmy Cliff’s performance was the highlight of clarity in a reggae festival that sometimes sounded more like a cacophony.Reading Denis Rothman’s book on Artificial Intelligence had the same effect on me as that live moment. In today’s market of AI related books and articles, his voice is one of clarity based on both personal experience (yes, he was there at the beginning of chat bots, deep learning and reinforced learning), and on a critical semiotic mind that goes beyond the code to explain how the codes come to signify, and how that signification can be understood and re-interpreted by us, the users.I read this book as a non-coder, but as an anthropologist, interested in how AI is produced and produces in its turn a new environment in which we work and live. For this reason I skipped the large part dedicated to pure coding and concentrated on the practical applications proposed, as well as on the interpretative part based on mathematics. As such, the chapter on emotion and chatbots was highly rewarding.As someone who worked in consulting and evaluation, I found the chapter of “Evaluation Beyond Human Analytic Capacity” highly informative. It shows how AI applied in management can transform mechanisms of command and control for a greater efficiency and may engender better collaboration between humans (and between humans and machines). As such, this book is indispensable for the future of management and leadership practices.The book’s last 3 chapters represent the final reward for any inquisitive mind that wants to go beyond the technicalities of AI and its application into meditating on the future of merging the two types of codes: genetic and human made computational codes - and how the future may look like in the quantum computational world. If it sounds like cyberpunk, it is because it may as well be. Only that with Denis Rothman’s book, and under his critical and informed perspective, the future is here.If your life is touched by AI - and if you are reading this, it surely is - this book is indispensable.

Artificial Intelligence By Example: Acquire advanced AI, machine learning, and deep learning design skills , Second Edition

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