Deep learning offers a powerful suite of techniques for building predictive models and creating value from large stores of digital data that are increasingly common in domains as diverse as manufacturing, finance, advertising, and healthcare. Deep learning models have achieved near or better than human performance in machine translation, speech recognition, and image classification and recently beat the world’s best human Go players, a feat that was believed to be decades away.
The rise of deep learning has several implications for data mining and analytics. First, building successful predictive models depends less on designing good features than it does upon matching architectures to problems and tuning hyperparameters. These in turn require a new set of skills and intuitions among practitioners. Second, deep learning models are computationally expensive to train and validate. Training even simple models on modestly sized datasets can require specialized hardware like GPUs while state-of-the-art performance featured in popular press often involves training large models with massive datasets on complex distributed compute platforms.
Dave Kale, Susan Eraly, and Josh Patterson provide a practical introduction to training neural networks using Deeplearning4j (DL4J), the open source, Java-based deep learning framework. Through both presented material and hands-on exercises, you’ll learn how to meet the challenges that arise when you apply deep learning to your business problems as Dave, Melanie, Susan, and Josh walk you through using DL4J to build and train several canonical neural net architectures, including a convolutional network for image classification and a recurrent neural net for sequence modeling.
David Kale is a deep learning engineer at Skymind and a PhD candidate in computer science at the University of Southern California (advised by Greg Ver Steeg of the USC Information Sciences Institute). David’s research uses machine learning to extract insights from digital data in high-impact domains, such as healthcare. Recently, he has pioneered the application of recurrent neural nets to modern electronic health records data. At Skymind, he is developing the ScalNet Scala API for DL4J and working on model interoperability between DL4J and other major frameworks. David organizes the Machine Learning and Healthcare Conference (MLHC), is a cofounder of Podimetrics, and serves as a judge in the Qualcomm Tricorder XPRIZE competition. David is supported by the Alfred E. Mann Innovation in Engineering Fellowship.
Susan Eraly is a software engineer at Skymind, where she contributes to Deeplearning4j. Previously, Susan worked as a senior ASIC engineer at NVIDIA and as a data scientist in residence at Galvanize.
Josh Patterson is the director of field engineering for Skymind. Previously, Josh ran a big data consultancy, worked as a principal solutions architect at Cloudera, and was an engineer at the Tennessee Valley Authority, where he was responsible for bringing Hadoop into the smart grid during his involvement in the openPDC project. Josh is a graduate of the University of Tennessee at Chattanooga with a master of computer science, where he did research in mesh networks and social insect swarm algorithms. Josh is a cofounder of the DL4J open source deep learning project and is a coauthor on the upcoming O’Reilly title Deep Learning: A Practitioner’s Approach. Josh has over 15 years’ experience in software development and continues to contribute to projects such as DL4J, Canova, Apache Mahout, Metronome, IterativeReduce, openPDC, and JMotif.
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