Math for Deep Learning: What You Need to Know to Understand Neural Networks Kindle Edition

4.7 out of 5 stars 9 ratings
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ISBN-13: 978-1718501904
ISBN-10: 1718501900
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Editorial Reviews


"What makes Math for Deep Learning a stand-out, is that it focuses on providing a sufficient mathematical foundation for deep learning, rather than attempting to cover all of deep learning, and introduce the needed math along the way. Those eager to master deep learning are sure to benefit from this foundation-before-house approach."
–Ed Scott, Ph.D., Solutions Architect & IT Enthusiast

About the Author

Ronald T. Kneusel earned a PhD in machine learning from the University of Colorado, Boulder. He has over 20 years of machine learning industry experience. Kneusel is also the author of Numbers and Computers (2nd ed., Springer 2017), Random Numbers and Computers (Springer 2018), and Practical Deep Learning: A Python-Based Introduction (No Starch Press 2021). --This text refers to the paperback edition.

Product details

  • ASIN ‏ : ‎ B096JXMQLM
  • Publisher ‏ : ‎ No Starch Press (November 23, 2021)
  • Publication date ‏ : ‎ November 23, 2021
  • Language ‏ : ‎ English
  • File size ‏ : ‎ 26048 KB
  • Text-to-Speech ‏ : ‎ Enabled
  • Enhanced typesetting ‏ : ‎ Enabled
  • X-Ray ‏ : ‎ Not Enabled
  • Word Wise ‏ : ‎ Not Enabled
  • Print length ‏ : ‎ 345 pages
  • Page numbers source ISBN ‏ : ‎ 1718501900
  • Lending ‏ : ‎ Not Enabled
  • Customer Reviews:
    4.7 out of 5 stars 9 ratings

About the author

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My infatuation with computers began with an Apple II in 1981. I've been active in machine learning since 2003, and deep learning since before AlexNet was a thing.

My background includes a Ph.D. in computer science from the University of Colorado, Boulder (deep learning), and an M.S. in physics from Michigan State University. By day, I work in industry building deep learning systems. By night, I type away on my keyboard generating the books you see here. I sincerely hope that if you explore my books, you gain as much enjoyment from them as I had in writing them.


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4.7 out of 5 stars
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Reviewed in the United States on January 18, 2022
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