With the abundance of deep learning frameworks available today, it can be difficult to know what to choose for any particular application. Given the contrasting strengths and weaknesses of these frameworks, the ability to work with and switch between more than one is particularly important. Recent Cloudera blogs have shown how examples of applying deep learning on the Cloudera ecosystem using popular frameworks Deeplearning4j, BigDL, and Keras+TensorFlow.
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,
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).
There are two clear trends in the big-data ecosystem: the growth of machine learning use cases that leverage large distributed data sets, and the growth of Spark’s Machine Learning libraries (often referred to as MLlib) for these use cases. In fact, Spark’s MLlib library is arguably the leading solution for machine learning on large distributed data sets.
Intel and Cloudera have collaborated to speed up Spark’s ML algorithms, via integration with Intel’s Math Kernel Library (Intel® MKL).