£34.84

Springer Geometry of Deep Learning - Signal Processing Book

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Description

Gain a deeper understanding of neural networks through a unique mathematical lens. Geometry of Deep Learning: A Signal Processing Perspective offers students a unified way to view deep learning, moving beyond simple implementation techniques. This book presents deep learning as the ultimate form of signal processing, providing a theoretical foundation that connects modern AI with established mathematical principles. By exploring the geometric aspects of these models, readers can grasp how complex data structures are processed and understood. The text provides a comprehensive overview of classical kernel machine learning approaches, clearly explaining their specific advantages and limitations. This approach helps bridge the gap between traditional signal processing and contemporary deep learning methods, making it an essential resource for those studying mathematics in industry and advanced machine learning theory.

Key Features

Provides a unified perspective on deep learning through the lens of geometry and signal processing.

Explains deep learning as an advanced form of signal processing rather than just an implementation method.

Includes a detailed overview of classical kernel machine learning approaches for better context.

Analyzes the advantages and limitations of existing machine learning techniques to build foundational knowledge.

Offers mathematical insights designed to help students understand the basic building blocks of deep learning.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
07 January 2023
Listed Since
10 December 2022

Barcode

No barcode data available

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