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A**R
A complex subject covered in depth
This book was inspired by my work at Oxford UniversityI have known Amita from her previous book (co-authored with Antonio Gulli) but I have not contributed to this bookBased on my experience in teaching AI for IoT, it is not an easy subject due to its vast scope.This book first covers the Principles and then lays the groundwork in terms of tools(ex keras), datasets(ex air quality) and File formats (ex JSON)The book then cover Machine Learning in depth(linear regression, Logistic regression, SVMs, Naive Bayes, Decision trees, Ensemble learning ) followed by Deep Learning(Multi layer perceptron, CNN, RNN, Autoencoders) and then covers more complex algorithms(Genetic Algorithms, Reinforcement Learning and GANs). This is followed by Distributed AI(Spark) followed by application areas (Smart homes, Smart cities and Industrial IoT)The book then combines these ideas together in the last sectionOverall, it is a comprehensive and a hand-on approachWhat could be improved? More coverage on the Cloud and the Edge technologiesWhat’s the best feature? The hands-on (coding based) approachOverall, its very much recommended
A**R
Practical and Insightful—Ideal for IoT and AI Professionals
As someone with deep experience in AI and IoT systems, I found AI and IoT: From Foundations to Smart Cities to be an outstanding resource. It offers a well-balanced mix of foundational theory and hands-on implementation, covering everything from edge computing and machine learning to advanced topics like reinforcement learning and distributed AI.The progression from basic concepts to real-world applications is thoughtfully designed, making it suitable for both beginners and professionals. The practical examples and use of modern tools like Keras, TinyML, and cloud platforms make this book highly relevant for today's AI-powered IoT landscape.Highly recommended for engineers, data scientists, and product leaders looking to build intelligent, scalable IoT solutions.
N**H
Must Read Book
Must read book on Artificial Intelligence, machine learning and deep learning for IoT. It also cover Genetic Algorithms, Reinforcement learning and Generative Models for IoT.
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