To see the ‘Sheet book‘ and ‘Table of associated records‘ go the bottom, Pag. 2Pag. 3. ( 👩‍🎓 🖥 For free online version, on the image).


A review of the book for research, educational and recommendations.


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Recommendation numbered, Nº: 07022020p1


🔘 Book page:

📘 url personal use : |🔓 1 Openaccess – 🛒 2 to buy | (Copy & paste at the browser)

-Creative Commons address: deeplearningbook.org
-Paid version address: amzn.to/2vTnmBi

Summary

“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 “. (Amazon book review).

Chapters

Part I: Applied Math and Machine Learning Basics

  • Linear Algebra
  • Probability and Information Theory
  • Numerical Computation
  • Machine Learning Basics

Part II: Modern Practical Deep Networks

  • Deep Feedforward Networks
  • Regularization for Deep Learning
  • Optimization for Training Deep Models
  • Convolutional Networks
  • Sequence Modeling: Recurrent and Recursive Nets
  • Practical Methodology
  • Applications

Part III: Deep Learning Research

  • Linear Factor Models
  • Autoencoders
  • Representation Learning
  • Structured Probabilistic Models for Deep Learning
  • Monte Carlo Methods
  • Confronting the Partition Function
  • Approximate Inference
  • Deep Generative Models

Author

[Unofficial biography. For informational purposes only]

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Ian Goodfellow

Researcher working in machine learning, currently employed at Apple Inc. as its director of machine learning in the Special Projects Group. He was previously employed as a research scientist at Google Brain. He has made several contributions to the field of deep learning. (Wikipedia).

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Yoshua Bengio

Professor, University of Montreal (Computer Sc. & Op. Res.), Mila, CIFAR, CRM, IVADO, REPARTI, GRSNC. (Google Scholar).

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Aaron Courville

Assistant Professor in the Department of Computer Science and Operations Research (DIRO) at the University of Montreal, and member of Mila – Quebec Artificial Intelligence Institute. (Mila Website).


Please, thank the author and Publisher


Thank you very much for this work to @goodfellow_ian et al, via @States_AI_IA #deeplearning #openscience #openaccess #ai #artificialintelligence #ia #thebibleai #ebook #free