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Deep Learning : A Visual Approach by Andrew Glassner (2021, Hardcover)
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Deep Learning : A Visual Approach, Paperback by Glassner, Andrew, ISBN 1718500726, ISBN-13 9781718500723, Brand New, Free shipping in the US "A practical, thorough introduction to deep learning, without the usage of advanced math or programming. Covers topics such as image classification, text generation, and the machine learning techniques that are the basis of modern AI"--
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About this product
Product Identifiers
PublisherNo Starch Press, Incorporated
ISBN-101718500726
ISBN-139781718500723
eBay Product ID (ePID)18038795208
Product Key Features
Number of Pages768 Pages
LanguageEnglish
Publication NameDeep Learning : a Visual Approach
SubjectNatural Language Processing, Neural Networks, General
Publication Year2021
TypeTextbook
AuthorAndrew Glassner
Subject AreaComputers, Science
FormatHardcover
Dimensions
Item Height1.5 in
Item Weight59 Oz
Item Length9.3 in
Item Width7.1 in
Additional Product Features
Intended AudienceTrade
LCCN2020-047326
Dewey Edition23
Reviews"Andrew is famous for his ability to teach complex topics that blend mathematics and algorithms, and this work I think is his best yet." -- Peter Shirley, Distinguished Research Engineer, Nvidia "I would recommend that anyone entering this area, or even already familiar with the subject, read it cover-to-cover to firmly ground their understanding." -- Richard Szeliski, author of Computer Vision: Algorithms and Applications, "For a visual person like myself, Andrew's approach makes these Deep Learning concepts much more accessible than the typical algebraic treatments. Andrew is famous for his ability to teach complex topics that blend mathematics and algorithms, and this work I think is his best yet." --Peter Shirley, Distinguished Research Engineer, Nvidia, "Andrew is famous for his ability to teach complex topics that blend mathematics and algorithms, and this work I think is his best yet." -- Peter Shirley, Distinguished Research Engineer, Nvidia "I would recommend that anyone entering this area, or even already familiar with the subject, read it cover-to-cover to firmly ground their understanding." -- Richard Szeliski, author of Computer Vision: Algorithms and Applications "This is a comprehensive--yet easy to understand--book about complex concepts and algorithms. Andrew Glassner demonstrates that visualizing concepts as graphs is a tremendous benefit to easy cognition." --Thomas Frisendal, author of Graph Data Modeling for NoSQL and SQL
IllustratedYes
Dewey Decimal006.31
Table Of ContentPart I: Foundational Ideas 1. An Overview of Machine Learning Techniques 2. Essential Statistical Ideas 3. Probability 4. Bayes' Rule 5. Curves and Surfaces 6. Information Theory Part II: Basic Machine Learning 7. Classification 8. Training and Testing 9. Overfitting and Underfitting 10. Data Preparation 11. Classifiers 12. Ensembles Part III: Deep Learning Basics 13. Neural Networks 14. Backpropagation 15. Optimizers Part IV: Beyond the Basics 16. Convolutional Neural Networks 17. Convnets in Practice 18. Recurrent Neural Networks 19. Autoencoders 20. Reinforcement Learning 21. Generative Adversarial Networks 22. Creative Applications Index
SynopsisAn 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., A richly-illustrated, full-color introduction to deep learning that offers visual and conceptual explanations instead of equations. You'll learn how to use key deep learning algorithms without the need for complex math. Ever since computers began beating us at chess, they've been getting better at a wide range of human activities, from writing songs and generating news articles to helping doctors provide healthcare. Deep learning is the source of many of these breakthroughs, and its remarkable ability to find patterns hiding in data has made it the fastest growing field in artificial intelligence (AI). Digital assistants on our phones use deep learning to understand and respond intelligently to voice commands; automotive systems use it to safely navigate road hazards; online platforms use it to deliver personalized suggestions for movies and books - the possibilities are endless. Deep Learning: A Visual Approach is for anyone who wants to understand this fascinating field in depth, but without any of the advanced math and programming usually required to grasp its internals. If you want to know how these tools work, and use them yourself, the answers are all within these pages. And, if you're ready to write your own programs, there are also plenty of supplemental Python notebooks in the accompanying Github repository to get you going. The book's conversational style, extensive color illustrations, illuminating analogies, and real-world examples expertly explain the key concepts in deep learning, including: - How text generators create novel stories and articles - How deep learning systems learn to play and win at human games - How image classification systems identify objects or people in a photo - How to think about probabilities in a way that's useful to everyday life - How to use the machine learning techniques that form the core of modern AI Intellectual adventurers of all kinds can use the powerful ideas covered in Deep Learning: A Visual Approach to build intelligent systems that help us better understand the world and everyone who lives in it. It's the future of AI, and this book allows you to fully envision it. Full Color Illustrations, 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.
LC Classification NumberQ325.5.G58 2021
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- 17 Dec, 2022
The best book on deep learning foundations
Best book for anyone to get the intuition of deep learning. Instead of going to multiple places for learning the foundations neededVerified purchase: YesCondition: Pre-owned
