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 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  Nonexclusive license to distribute
 Paperback: N/A
 eBook: PDF (601 pages) and PostScript
 Language: English
 ISBN10/ASIN: N/A
 ISBN13: 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 fullyconnected 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).
About the Author(s) N/A
 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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