Deep Learning: A Practitioner’s Approach – Josh Patterson
Deep Learning: A Practitioner’s Approach – Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning—especially deep neural networks—make a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks.
Authors Adam Gibson and Josh Patterson provide theory on deep learning before introducing their open-source Deeplearning4j (DL4J) library for developing production-class workflows. Through real-world examples, you’ll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J.
Dive into machine learning concepts in general, as well as deep learning in particular
Understand how deep networks evolved from neural network fundamentals
Explore the major deep network architectures, including Convolutional and Recurrent
Learn how to map specific deep networks to the right problem
Walk through the fundamentals of tuning general neural networks and specific deep network architectures
Use vectorization techniques for different data types with DataVec, DL4J’s workflow tool
Learn how to use DL4J natively on Spark and Hadoop
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.
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
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