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.

Who is this book 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.

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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SR_WA Dec 01, 2023

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

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Tamzid Bhuiyan May 19, 2021

information are helpful , good for students

hawkinflight Jul 18, 2020

I was very interested in looking at this book for the last two chapters - Neuromorphic Computing (NC) and Quantum Computing (QC). I knew nothing about NC, and I have been studying QC. I have some background with machine learning. I found these two chapters to be very fun, informative and easy to read.From the Neuromorphic Computing chapter I learned that Intel Research is working on a neurocomputing chip, containing hundreds to thousands of neurons, and that those chips could be connected to make a network. Given that there are 100 billion neurons in a human brain, it would take a huge number of chips to be equivalent. Neurocomputing software, Nengo, is mentioned, which I found interesting, also in the Further Reading section a book written by the author of Nengo is mentioned which describes "how to build a brain". In the chapter, a distinction is made between the neuromorphic computing approach and neural nets. It is also mentioned that Google's TPU is a specialized chip, that is, hardware designed to work well with the TensorFlow software, and that we can expect more of this in the future.The Quantum Computing chapter is brief, but nice in that it gives a quick introduction, and connects with the previous chapter, Neuromorphic Computing. It is mentioned that in the future, a quantum computer could represent a brain, which could be called from a classical computer. A nice, quick, back-of-the-napkin like calculation is done to demonstrate the difference between linear (classical) and exponential (quantum) growth. The author gives an example of a quantum algorithm he wrote which processes some data and seems to return a number which could be interpreted as a movie "recommended" or not. There's not enough description, I think, of the gates applied to get a good feel about what was done and why, but it's nice in that it's really "to the point". Quantum algorithms seem like a pretty hard topic, but this example is motivating, causing me to think - hey, that sounds neat, it's different from what I had been thinking, what is quantum ML like? and, to seek out other sources on the topic.Finally, at the end of the book there is a section - Answers to the Questions, which were asked at the end of each chapter. I enjoyed reading through these to test myself and check the answers against my thoughts. I liked reading the sections about: 1) the self-driving vehicles, which included a challenge question - would you like to design an autonomous driving system for your city? 2) adding emotional intelligence to chat bots 3) combining methods, for example, reinforcement learning + deep learning, decision trees + k-means clustering, genetic algorithms + neural nets.The author is very bullish on quantum computers, though there is no guarantee that QC's can be developed to the point of being functional, for a real problem. I too, however, am optimistic. Via the chapter questions and answers, the author does comment on what existing systems cannot do today; for example, that there currently is no "general AI", like a human, but instead, just "narrow AI", as in, specific tasks only.I like the breadth.

siebo Jun 02, 2020

This book took me much deeper on my journey into AI and ML. It takes you through a series and algos and sub-disciplines of Machine Learning explaining each one in a way that is both thorough and concise. It does a good job of instilling the problem solving and modeling process that is necessary to design and implement different approaches. The examples are much like what you are likely to encounter in professional settings rather than the "toy" apps that some books use as examples. Coming from a Python background, I found the code samples very approachable. My statistics background is not as strong, so for some of the concepts, I had to do a bit of side reading to get up to speed. The authors writing style is clear and straightforward, combined with how he narrates his thought process, it gave me a lot of confidence to work with these AI techniques.

Trebor May 28, 2020

A topic such as AI is very complex and pretty difficult to describe, let alone in a single book. Most books cover purely from a theoretical explanation and others barely cover any theory and use pure use cases or examples to illustrate the properties of AI. This book seems to cover both very well. There are some interesting naming conventions and assumptions, but overall the book does accomplish what the title describes, you will learn AI by examples. Just the right balance of example and description.

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

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