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Product details

File Size: 21896 KB

Print Length: 538 pages

Simultaneous Device Usage: Unlimited

Publisher: O'Reilly Media; 1 edition (July 28, 2017)

Publication Date: July 28, 2017

Sold by: Amazon Digital Services LLC

Language: English

ASIN: B074D5YF1D

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#303,962 Paid in Kindle Store (See Top 100 Paid in Kindle Store)

I agree with the other three-star reviewers: the fundamental first three chapters are poorly organized and seem hastily written. Concepts jump from very basic to very advanced, some with little more than a mention (ex: just 3 sentences on clustering), so the reader does not get "a full journey through deep learning", at least not a useful one. The title "Deep Learning" is misleading: it should really be something like "A Practical Guide to Deep Learning with DL4J".

Have you been following the Artificial Intelligence in computing fad for the last decade or so? I have since I designed an AI based medical device to regulate human blood pressure in the ICU and received FDA approval for my design to be used in the US. I wanted to know what others were doing in applying AI in various computing categories and which ones were similar to natural neural networks in mammalian brains. But the literature was too scattered and apparently incomparable to make sense in general to be useful.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.

The book is based on the powerful open source deeplearning4j framework, which aims mainly for Java and other JVM languages (e.g. Scala).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.

OK, but there are better books, and this is not necessarily practitioner oriented ... it's more a less a rehash of material that is online.

I own this book and kind of disappointed after buying it that all codes are only in JAVA. Python is the status quo of deep learning from the point of view of hardcore data scientists. Possibly the authors can also write a book in Python.

Excellent book which delves into DL4J platform and Deep learning science as a whole. It list papers you can read to further your research. I have completed reading the book and I am rereading it again for firmer understanding. It is a great book to learn how to use a Java based platform for deep learning.

This is an excellent book. I have hundreds of papers and books on Neural Nets from the time of Rosenblatt's Perceptron on through autoencoders, recurrent NNs, convolutional NNs, RBM's, DNN's, greedy pretraining, Kolmogrov's universal approximation theorem, optimization methods for weight training, and more.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.

I think the book is very well laid out and easy to read. Personally, I read half the book in 2 hours. You cannot say that about many books in engineering school, especially for a graduate level class. The font isn't to small and it's not one of those picture-less pray that your eyes don't fall out kind of books either.Lastly, you could not watch Youtube or Google quick enough to get the amount of simple information that this book gives you within the first hour of reading. As someone who is specializing in the deep learning field, this book teaches you the wording and nomenclature to sound smart in your papers and know what your talking about. This is the most I have ever read in a STEM field textbook and did not despise it.

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