Description
Introduction to Deep Learning. Understanding the evolution of artificial intelligence from basic mathematical principles to today’s complex neural networks is key to success in learning this technology. This textbook, written by Sandro Scansi, is a coherent, accessible, and engaging guide that explores the historical and evolutionary history of the fundamental concepts of deep learning, from its roots in logical computation to modern connectionist models. The present work strives to strike a careful balance between theoretical mathematical foundations and practical implementations, so that undergraduate and graduate students can gain a comprehensive perspective.
The book begins by outlining the essential mathematical prerequisites and then gradually introduces the main structures of artificial neural networks, including feedforward networks, convolutional neural networks (CNN), recurrent neural networks (RNN), and autoencoders. The author explains in fluent language fundamental algorithms such as backpropagation and shows how deep learning has successfully conquered traditional AI domains such as reasoning and programming, which were once the preserve of logic systems.
Book Features
- A comprehensive review of the historical and conceptual history of artificial intelligence from mathematical logic to today’s deep models.
- Complete coverage of the main neural network architectures, including convolutional, recurrent, and autoencoders.
- Detailed explanation of basic mathematics and critical algorithms such as error backpropagation with concrete examples.
- Structured Design as a suitable textbook for students of computer science and engineering.
- Providing intuitive insights and practical examples to facilitate the Mastery process and continuous learning in the field of artificial intelligence.
Book specifications
- Publisher: Springer
- Instructor/Author: Sandro Skansi
- Number of pages: 196
- Number of chapters: 11
- Format: PDF
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