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(Ebook) Deep Learning: A Visual Approach by Glassner, Andrew ISBN 9781718500723, 9781718500730, 1718500726, 1718500734, 2020047326, 2020047327

  • SKU: EBN-32736510
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Instant download (eBook) Deep Learning: A Visual Approach after payment.
Authors:Glassner, Andrew
Pages:776 pages.
Year:2021
Editon:Illustrated
Publisher:No Starch Press
Language:english
File Size:62.26 MB
Format:epub
ISBNS:9781718500723, 9781718500730, 1718500726, 1718500734, 2020047326, 2020047327
Categories: Ebooks

Product desciption

(Ebook) Deep Learning: A Visual Approach by Glassner, Andrew ISBN 9781718500723, 9781718500730, 1718500726, 1718500734, 2020047326, 2020047327

An accessible, highly-illustrated introduction to deep learning that offers visual and conceptual explanations instead of equations. Readers learn how to use key deep learning algorithms without the need for complex math. Deep Learning algorithms can start with mountains of data and measurements and turn them into useful and meaningful patterns. This book is for people with sharp minds who may lack the math background necessary to deal with equations or complex mechanics, but who nevertheless want to understand the "how" of deep learning, and actually use these tools for themselves. Deep Learning- A Visual Approach helps demystify the algorithms that enable computers to drive cars, win chess tournaments, and create symphonies, while giving readers the tools necessary to build their own systems to help them find the information hiding within their own data, create "deep dream" artwork, or create new stories in the style of their favorite authors. Scientists, artists, programmers, managers, hobbyists, and intellectual adventurers of all kinds can use deep learning tools to make new discoveries and create new kinds of art and intelligent systems. The book's friendly, informal approach to deep learning demonstrates the concepts visually. There's no math beyond the occasional multiplication and no programming experience is required. By the end of the book, readers will be equipped to understand modern deep learning systems, and anyone who wants to program and train their own deep learning networks will be able to dive into the library of their choice and start implementing with knowledge and confidence.
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