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Artificial Intelligence Engines
Artificial Intelligence Engines

Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning

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Profile Icon James V Stone
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€4.99 €7.99
eBook Nov 2024 217 pages 1st Edition
eBook
€4.99 €7.99
Arrow left icon
Profile Icon James V Stone
Arrow right icon
€4.99 €7.99
eBook Nov 2024 217 pages 1st Edition
eBook
€4.99 €7.99
eBook
€4.99 €7.99

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Key benefits

  • Comprehensive breakdown of foundational and advanced AI algorithms with practical insights.
  • Clear explanations of deep learning architectures, including GANs, autoencoders, and CNNs.
  • Step-by-step guidance on implementing mathematical concepts in real-world AI applications.

Description

This book is a comprehensive guide to the mathematics behind artificial intelligence engines, taking readers from foundational concepts to advanced applications. It begins with an introduction to artificial neural networks, exploring topics like perceptrons, linear associative networks, and gradient descent. Practical examples accompany each chapter, making complex mathematical principles accessible, even for those with limited prior knowledge. The book's detailed structure covers key algorithms like backpropagation, Hopfield networks, and Boltzmann machines, advancing to deep restricted Boltzmann machines, variational autoencoders, and convolutional neural networks. Modern topics such as generative adversarial networks, reinforcement learning, and capsule networks are explored in depth. Each section connects theory to real-world AI applications, helping readers understand how these techniques are used in practice. Ideal for students, researchers, and AI enthusiasts, the book balances theoretical depth with practical insights. Basic mathematical knowledge or foundation is recommended, allowing readers to fully engage with the content. This book serves as an accessible yet thorough resource for anyone eager to dive deeper into artificial intelligence and machine learning.

Who is this book for?

Students and professionals interested in artificial intelligence and machine learning will find this book an invaluable resource. A basic mathematical knowledge or foundation is recommended to follow the concepts effectively. Suitable for researchers, engineers, and AI enthusiasts aiming to strengthen their theoretical understanding while gaining practical implementation skills.

What you will learn

  • Master the fundamentals of artificial neural networks effectively.
  • Apply gradient descent techniques for training neural networks.
  • Design perceptrons to solve classification and logic problems.
  • Explore the mechanics of backpropagation in learning processes.
  • Analyze Boltzmann machines for generative model applications.
  • Examine reinforcement learning for decision-making scenarios.

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Nov 25, 2024
Length: 217 pages
Edition : 1st
Language : English
ISBN-13 : 9781837024063
Category :

What do you get with eBook?

Product feature icon Instant access to your Digital eBook purchase
Product feature icon Download this book in EPUB and PDF formats
Product feature icon DRM FREE - Read whenever, wherever and however you want
OR
Modal Close icon
Payment Processing...
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Product Details

Publication date : Nov 25, 2024
Length: 217 pages
Edition : 1st
Language : English
ISBN-13 : 9781837024063
Category :

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