• Title: Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory
• Author(s) Arnulf Jentzen, Benno Kuckuck, Philippe von Wurstemberger
• Publisher: arXiv (October 31, 2023); eBook (arXiv Licensed)
• License(s): arXiv - Non-exclusive license to distribute
• Paperback: N/A
• eBook: PDF (601 pages) and PostScript
• Language: English
• ISBN-10/ASIN: N/A
• ISBN-13: N/A
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Book Description

This book aims to provide an introduction to the topic of deep learning algorithms. We review essential components of deep learning algorithms in full mathematical detail including different Artificial Neural Network (ANN) architectures (such as fully-connected feedforward ANNs, convolutional ANNs, recurrent ANNs, residual ANNs, and ANNs with batch normalization) and different optimization algorithms (such as the basic Stochastic Gradient Descent (SGD) method, accelerated methods, and adaptive methods).

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• Deep Learning and Neural Networks
• Linear and Matrix Algebra
• Calculus and Mathematical Analysis
• Statistics
• Probability and Stochastic Processes

• Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory
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