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

Denis Rothman

$9.99 ~~$29.99~~

4.6 (17 Ratings)

eBook Feb 2020 578 pages 2nd Edition

Denis Rothman

$9.99 ~~$29.99~~

4.6 (17 Ratings)

eBook Feb 2020 578 pages 2nd Edition

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Key benefits

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

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

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Tamzid Bhuiyan May 19, 2021

information are helpful , good for students

Amazon Verified review

Jean-patrice Glafkides Mar 28, 2020

This is a big book that aim at showing all aspects of AI. It drives us trhu to almost all kind of AI as of now with sample. It helps me find quick information on some specific part of AI.

Laura Mar 24, 2020

This book covers a breath of concepts not typically found in a single resource. Valuable for understanding both the theory and the applications of AI development. Very insightful!

Pedro V. Marcal Mar 30, 2020

A long established mantra of AI is that 'data is program and program is data'. Nowhere is this more true than in Reinforcement Learning (RL) and Deep Learning (DL). Here there is no established theory, just established Algorithms. The author's book steps in and by careful building of examples illustrates the underlying principles behind RL and DL. Each example is explained by [1] explaining the basis of the required data. [2] a textual understanding of the problem. [3] the development of a Python based program to solve the problem.I recommend this book to those who wish to gain a better insight into RL and DL and are willing to work towards it.

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.

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

What do you get with eBook?