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TensorForce: A TensorFlow library for applied reinforcement learning – reinforce.io

This blogpost will give an introduction to the architecture and ideas behind TensorForce, a new reinforcement learning API built on top of TensorFlow. This post is about a practical question: How can the applied reinforcement learning community move from collections of scripts and individual examples closer to an API for reinforcement learning (RL) — a ‘tf-learn’ or ‘skikit-learn’ for RL? Before discussing ...

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Yahoo supercharges TensorFlow with Apache Spark 

Yahoo, model Apache Spark citizen and developer of CaffeOnSpark, which made it easier for developers building deep learning models in Caffe to scale with parallel processing, is open sourcing a new project called TensorFlowOnSpark. The pairing of Spark and TensorFlow should make the deep learning framework more attractive to developers who are creating models that need to run on large computing clusters. Fo ...

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Distributed Deep Learning with Apache Spark and Keras | Databases at CERN

In the following blog posts we study the topic of Distributed Deep Learning, or rather, how to parallelize gradient descent using data parallel methods. We start by laying out the theory, while supplying you with some intuition into the techniques we applied. At the end of this blog post, we conduct some experiments to evaluate how different optimization schemes perform in identical situations. We also intr ...

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Deep Learning on Databricks – The Databricks Blog

We are excited to announce the general availability of Graphic Processing Unit (GPU) and deep learning support on Databricks! This blog post will help users get started via a tutorial with helpful tips and resources, aimed at data scientists and engineers who need to run deep learning applications at scale. What’s new? Databricks now offers a simple way to leverage GPUs to power image processing, text analy ...

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