Databricks Integrates Spark and TensorFlow for Deep Learning

Since announcements late last year about Google open-sourcing TensorFlow, the company’s open-source library for machine learning, and previous coverage at InfoQ, the data-science community has had an opportunity to try out TensorFlow for their own projects. Databricks’ Tim Hunter demonstrates TensorFlow-generated model selection and at-scale neural network processing with Spark. Hunter describes an artificial neural network as mimicking the neurons in the visual cortex of the human brain, which when adequately trained can be used for processing complex input data like imagery or audio. Hunter detailed how he ran TensorFlow on various Spark configurations to parallelize hyperparameter tuning. Hunter stated that TensorFlow, currently available with Python and C++ support helped “automate the creation of training algorithms for neural networks of various shapes and sizes” for the purpose of training a…

Link to Full Article: Databricks Integrates Spark and TensorFlow for Deep Learning

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