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Age Group
18
Key Features
Deep Learning, Deep Learning by Ian Goodfellow, Deep, Learning
Width
2
Height
2
Weight
0.2
Title
Deep Learning
Publication Year
2016 2016
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faurd seller
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Ratings and reviews
3.8
Good
based on 145 ratings by
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1
Waste of money!
10 months ago
Please don't buy this book, this seller is a fraud. I received aw...
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Aryan Sharma
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23
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5
Worth every penny
1 year ago
Great Book, coloured pages, awesome page qualities, received hard...
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Suvrajit Mullick
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Verified Buyer
3
1
5
Terrific purchase
1 year ago
Great Book. Especially the first 4 chapters are great
Aman Kumar
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Verified Buyer
1
0
1
Don't waste your money
2 days ago
Don't buy, not because of content, because of the quality of the ...
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UJJWAL KUMAR MISHRA
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Verified Buyer
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1
Hated it!
6 months ago
Text is too small to read and binding is damaged. Not as expected...
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MUKESH PARMAR
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0
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5
Awesome
6 months ago
Need to spend some time. If you want read, take the space and sta...
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Venkata Medagam
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0
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5
Must buy!
6 months ago
Awesome
Flipkart Customer
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5
Must buy!
6 months ago
Very Informative Book 📕 on Deep Learning and its one of the most...
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Deep Bachhar
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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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