- Paperback: 532 pages
- Publisher: O'Reilly Media; 1 edition (August 19, 2017)
- Language: English
- ISBN-10: 1491914254
- ISBN-13: 978-1491914250
- Product Dimensions: 7 x 1.1 x 9.2 inches
- Shipping Weight: 1.8 pounds (View shipping rates and policies)
- Average Customer Review: 17 customer reviews
- Amazon Best Sellers Rank: #26,851 in Books (See Top 100 in Books)
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Deep Learning: A Practitioner's Approach 1st Edition
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From the Publisher
From the Preface
Who Should Read This Book?
As opposed to starting out with toy examples and building around those, we chose to start the book with a series of fundamentals to take you on a full journey through deep learning.
We feel that too many books leave out core topics that the enterprise practitioner often needs for a quick review. Based on our machine learning experiences in the field, we decided to lead-off with the materials that entry-level practitioners often need to brush up on to better support their deep learning projects.
You might want to skip Chapters 1 and 2 and get right to the deep learning fundamentals. However, we expect that you will appreciate having the material up front so that you can have a smooth glide path into the more difficult topics in deep learning that build on these principles. In the following sections, we suggest some reading strategies for different backgrounds.
The Enterprise Machine Learning Practitioner
We split this category into two subgroups:
- Practicing data scientist.
- Java engineer.
The Practicing Data Sceintist
This group typically builds models already and is fluent in the realm of data science. If this is you, you can probably skip Chapter 1 and you’ll want to lightly skim Chapter 2. We suggest moving on to Chapter 3 because you’ll probably be ready to jump into the fundamentals of deep networks.
The Java Engineer
The Enterprise ExecutiveJava engineers are typically tasked with integrating machine learning code with production systems. If this is you, starting with Chapter 1 will be interesting for you because it will give you a better understanding of the vernacular of data science. Appendix E should also be of keen interest to you because integration code for model scoring will typically touch ND4J’s API directly.
The Enterprise Executive
Some of our reviewers were executives of large Fortune 500 companies and appreciated the content from the perspective of getting a better grasp on what is happening in deep learning. One executive commented that it had 'been a minute' since college, and Chapter 1 was a nice review of concepts. If you’re an executive, we suggest that you begin with a quick skim of Chapter 1 to re-acclimate yourself to some terminology. You might want to skip the chapters that are heavy on APIs and examples, however.
If you’re an academic, you likely will want to skip Chapters 1 and 2 because graduate school will have already covered these topics. The chapters on tuning neural networks in general and then architecture-specific tuning will be of keen interest to you because this information is based on research and transcends any specific deep learning implementation. The coverage of ND4J will also be of interest to you if you prefer to do high-performance linear algebra on the Java Virtual Machine (JVM).
About the Author
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.
Top customer reviews
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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.
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.
Most recent customer reviews
- The first couple of chapters are great. The author does a good job at explaining the different network architectures and how they work.Read more