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MIT Press Ltd Deep Learning - Deep Learning (English, Hardcover,...
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Product highlights
Title
Deep Learning
Imprint
MIT Press
Publication Year
2016 November
Book Type
hardcover
Exam
computer
Table of Contents
[T]he AI bible... the text should be mandatory reading by all data scientists and machine learning practitioners to get a proper foothold in this rapidly growing area of next-gen technology. -Daniel D. Gutierrez, insideBIGDATA
Product Form
Hardcover
Publisher
MIT Press Ltd
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4.7
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5
Worth every penny
7 years ago
Must have for anyone who are serious about learning and Understan...
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Raghavendra Kumar
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6
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4
Nice product
7 years ago
Got this book delivered in not so good condition , the edges were...
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14
3
5
Excellent
7 years ago
Flipkart delivered the book in a very safe hard-board cover withi...
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Alok Kumar
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4
Very Good
5 years ago
Writing this after 1 week of reading- Book content is excellent,...
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5
Excellent
4 years ago
Received original color edition hardcover with excellent paper qu...
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5
Just wow!
4 years ago
Not for beginners thoz who are week in math. First learn basics o...
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5
Fabulous!
3 years ago
Really good book for someone who wants to dive deep into understa...
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5
Awesome
3 years ago
Nice book… with superb quality… highly recommended…
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An Introduction To A Broad Range Of Topics In Deep Learning, Covering Mathematical And Conceptual Background, Deep Learning Techniques Used In Industry, And Research Perspectives. “Written By Three Experts In The Field, Deep Learning Is The Only Comprehensive Book On The Subject.” —Elon Musk, Cochair Of OpenAI; Cofounder And CEO Of Tesla And SpaceX Deep Learning Is A Form Of Machine Learning That Enables Computers To Learn From Experience And Understand The World In Terms Of A Hierarchy Of Concepts. Because The Computer Gathers Knowledge From Experience, There Is No Need For A Human Computer Operator To Formally Specify All The Knowledge That The Computer Needs. The Hierarchy Of Concepts Allows The Computer To Learn Complicated Concepts By Building Them Out Of Simpler Ones; A Graph Of These Hierarchies Would Be Many Layers Deep. This Book Introduces A Broad Range Of Topics In Deep Learning. The Text Offers Mathematical And Conceptual Background, Covering Relevant Concepts In Linear Algebra, Probability Theory And Information Theory, Numerical Computation, And Machine Learning. It Describes Deep Learning Techniques Used By Practitioners In Industry, Including Deep Feedforward Networks, Regularization, Optimization Algorithms, Convolutional Networks, Sequence Modeling, And Practical Methodology; And It Surveys Such Applications As Natural Language Processing, Speech Recognition, Computer Vision, Online Recommendation Systems, Bioinformatics, And Videogames. Finally, The Book Offers Research Perspectives, Covering Such Theoretical Topics As Linear Factor Models, Autoencoders, Representation Learning, Structured Probabilistic Models, Monte Carlo Methods, The Partition Function, Approximate Inference, And Deep Generative Models. Deep Learning Can Be Used By Undergraduate Or Graduate Students Planning Careers In Either Industry Or Research, And By Software Engineers Who Want To Begin Using Deep Learning In Their Products Or Platforms. A Website Offers Supplementary Material For Both Readers And Instructors. Hardcover, Ian Goodfellow
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