Mathematical Aspects of Deep Learning (Cambridge University Press)
Friday, 10 February 2023

In recent years the development of new classification and regression algorithms based on deep learning has led to a revolution in the fields of artificial intelligence, machine learning, and data analysis. In this book, Philipp Grohs and Gitta Kutyniok present the current mathematical understanding of deep learning methods from the point of view of the leading experts in the field.

<ASIN:1316516784>

The development of a theoretical foundation to guarantee the success of these algorithms constitutes one of the most active and exciting research topics in applied mathematics.

Author: Philipp Grohs and Gitta Kutyniok
Publisher: Cambridge University Press
Date: February 2023
Pages: 492
ISBN: 978-1316516782
Print:1316516784
Kindle:B0BMW15BV5
Audience: Developers interested in deep learning
Level: Intermediate/Advanced
Category: Mathematics and Artificial Intelligence

 

mathdeep

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Python Programming and Visualization for Scientists 2nd Ed

Author: Alex DeCaria and Grant Petty
Publisher: Sundog Publishing
Pages: 372
ISBN: 978-0972903356
Print: 0972903356
Audience: Scientists wanting to use Python
Rating: 2
Reviewer: Mike James
Visualization - a difficult topic and difficult to see how to explain the ideas in a book.



Foundational Python For Data Science

Author: Kennedy Behrman
Publisher: Pearson
Pages:256
ISBN: 978-0136624356
Print: 0136624359
Kindle: B095Y6G2QV
Audience: Data scientists
Rating: 4.5
Reviewer: Kay Ewbank

This book sets out to be a simple introduction to Python, specifically how to use it to work with data.


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