- Taschenbuch: 507 Seiten
- Verlag: O'Reilly UK Ltd.; Auflage: 1 (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. 6.671 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)
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Deep Learning: The Definitive Guide: A Practitioner's Approach (Englisch) Taschenbuch – 19. August 2017
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Über den Autor und weitere Mitwirkende
Josh Patterson currently runs a consultancy in the big data machine learning / deep learning space. Previously Josh worked as a Principal Solutions Architect at Cloudera and as a machine learning / distributed systems engineer at the Tennessee Valley Authority where he broughtHadoop into the smart grid with the openPDC project. Josh has a Masters in Computer Science from the University of Tennessee at Chattanooga where he did published research on mesh networks (tinyOS) and social insect optimization algorithms. Josh has over 17 years in software development and is very active in the open source space contributing to projects such as deeplearning4j, Apache Mahout, Metronome, IterativeReduce, openPDC, and JMotif.
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.
I found this book to provide a conceptual overview of the DNNs and the architectures (feed forward, deep belief, unsupervised pre-trained, convolutional, recurrent, long and short term memory, and recursive, networks). The book provides the conceptual connective tissue that are the muscles that the practitioner must bond to the architectural bones to move forward in Deep Learning. The book is a remarkable debrief by two lead developers of the DL4J framework; Josh Patterson and Adam Gibson. Every chapter offers new nuggets about how to apply their framework to real world ML problems, and about real world ML problems.
The ending chapters are about the actual application of the DL4j framework to practical problems, and how to use the framework with DL4j with Spark, the ND4J API, using GPU's, distributed training, and trouble shooting.
Unlike most books, many of which I buy for reference purposes to find what I need at various times, I am reading this one page-by-page to pick up all of the insightful observations. The book is an easy read for practitioners, and well worth the time, and modest price.
DL4J may provide some real competition to Tensorflow, and Caffe, especially in enterprise Java environments.
Hats off to Josh Patterson and Adam Gibson. Well done.