Category Archives: Cloudera Data Science Workbench

New in Cloudera Data Science Workbench 1.2: Usage Monitoring for Administrators

Categories: CDH Cloudera Data Science Workbench Data Science Performance

Cloudera Data Science Workbench (CDSW) provides data science teams with a self-service platform for quickly developing machine learning workloads in their preferred language, with secure access to enterprise data and simple provisioning of compute. Individuals can request schedulable resources (e.g. compute, memory, GPUs) on a shared cluster that is managed centrally.

While self-service provisioning of resources is critical to the rapid interaction cycle of data scientists, it can pose a challenge to administrators.

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Deep learning with Apache MXNet on Cloudera Data Science Workbench

Categories: CDH Cloudera Data Science Workbench Data Science

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.

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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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