Tag Archives: Deep Learning

Understanding how Deep Learning learns to play SET®

Categories: CDH Cloudera Data Science Workbench Data Science

In the past few years, deep learning has seen incredible success in image recognition applications. In this post I examine how to train a convolutional neural network to recognize playing card images from a game called SET®, explore the structure of the model to get some insight into what it is “seeing”, and present a webcam application that uses the deployed model in a near-realtime setting.

SET is a card game where the objective is to find triples of cards,

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How To Predict ICU Mortality with Digital Health Data, DL4J, Apache Spark and Cloudera

Categories: CDH Data Science Spark

Modeling EHR Data in Healthcare

In this case study, we take a look at modeling electronic health record (EHR) data with deep learning and Deeplearning4j (DL4J). We draw inspiration from recent research showing that carefully designed neural network architectures can learn effectively from the complex, messy data collected in EHRs. Specifically, we describe how to train an  long short-term memory recurrent neural network (LSTM RNN) to predict in-hospital mortality among patients hospitalized in the intensive care unit (ICU).

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Deep Learning with Intel’s BigDL and Apache Spark

Categories: CDH Data Science Hadoop Spark

Cloudera recently published a blog post on how to use Deeplearning4J (DL4J) along with Apache Hadoop and Apache Spark to get state-of-the-art results on an image recognition task. Continuing on a similar stream of work, in this post we discuss a viable alternative that is specifically designed to be used with Spark, and data available in Spark and Hadoop clusters via a Scala or Python API.

The Deep Learning landscape is still evolving.

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Deep learning on Apache Spark and Apache Hadoop with Deeplearning4j

Categories: Data Science Hadoop Spark

In late 2016, Ben Lorica of O’Reilly Media declared that “2017 will be the year the data science and big data community engage with AI technologies.” Deep learning on GPUs has pervaded universities and research organizations prior to 2017, but distributed deep learning on CPUs is now beginning to gain widespread adoption in a diverse set of companies and domains. While GPUs provide top-of-the-line performance in numerical computing, CPUs are also becoming more efficient and much of today’s existing hardware already has CPU computing power available in bulk.

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