- Taschenbuch: 507 Seiten
- Verlag: O'Reilly UK Ltd. (19. August 2017)
- Sprache: Englisch
- ISBN-10: 1491914254
- ISBN-13: 978-1491914250
- Größe und/oder Gewicht: 17,8 x 2,7 x 23,3 cm
- Durchschnittliche Kundenbewertung: 2 Kundenrezensionen
- Amazon Bestseller-Rang: Nr. 25.321 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)
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Deep Learning: The Definitive Guide: A Practitioner's Approach Taschenbuch – 19. August 2017
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Über den Autor und weitere Mitwirkende
Josh Patterson is CEO of Patterson Consulting, a solution integrator at the intersection of big data and applied machine learning. In this role, he brings his unique perspective blending a decade of big data experience and wide-ranging deep learning experience to Fortune 500 projects. At the Tennessee Valley Authority (TVA), Josh drove the integration of Apache Hadoop for large-scale data storage and processing of smart grid phasor measurement unit (PMU) data. Post-TVA, Josh was a principal solutions architect for a young Hadoop startup named Cloudera (CLDR), as employee 34. After leaving Cloudera, Josh co-founded the Deeplearning4j project and co-wrote Deep Learning: A Practitioner's Approach (O'Reilly Media). Josh was also the VP of Field Engineering for Skymind.
Adam Gibson is a deep-learning specialist based in San Francisco who works with Fortune 500 companies, hedge funds, PR firms and startup accelerators to create their machine-learning projects. Adam has a strong track record helping companies handle and interpret big realtime data. Adam has been a computer nerd since he was 13, and actively contributes to the open-source community through deeplearning4j.org.
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But it is based on a java lib, and you may have 5 pages of codes, and the theory is even less than what I wanted.
So a nice book but I will have to buy something else.
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Well, this is the book we've been looking for and it's about time! This is the gateway book to almost all of the methodologies used in developing AI computing. I still uniquely own the knowledge of developing AI by expert system design. But, in 500 pages this book covers the introduction to deep learning, fundamentals, architectures, concepts and models, tuning, data vectorization, and Spark data reduction with Hadoop. I found more areas of AI being uncovered here than I knew existed. What a bonanza!
Designers are all much richer now that we can incorporate these AI approaches into our thinking. Buy the book and become an AI expert overnight. There is just one caveat, you will have to buy additional references to get to the deep details of the learning process in each category. But at least, you will have the relative certainly of knowing that you have examined all of the known approaches and picked the one most appropriate to be successful for your application.
Deeplearning4j is an efficient and easy to use system and the book uncovers its potential very well. The book avoids the rather difficult theoretical discussions, and instead provides the necessary intuition for applications in real problems.
The book has strong focus on the application of deep learning models, and it presents clearly and in easy to understand way a lot of applications.
It is an excellent book, that can be used effectively with the more theoretical "Deep Learning" book of Ian Goodfellow, Yoshua Bengio, Aaron Courville, in order to gain both theoretical and applied insight on the emerging field of deep learning.
In a few words, it is a superb book, especially for Java/Scala programmers.