![]() Other than that all the steps below would work well in a MacOS(mine is a Mac Mini), or a Linux environment (I tried e.g. My Dell laptop happens to come with a low-end GPU GeForce 840M, not powerful but nonetheless it increases the demo's training speed by 3x times. HealthShare - we will use a HealthShare 2017.2.1 instance.Python 3 - included in the Anaconda package.Jupyter Notebook - included in the Anaconda package.Keras - a Python Deep Learning Library that sits on a Tensorflow or Theano engine.Theano- a Python library of computation engine for ML.Tensorflow - a machine learning platform with powerful computation engines. ![]() ![]() Anaconda - a data science platform popular with well structured package management.The demo kit we are trying to build here includes the following components: HealthShare is a data platform that provides an unified care record of patients for care providers.Ĭould we bind them all together into a single kit, and what could be the simplest possible approach to achieve this? Here let's try a first step on this topic together, before we consider what demo we can experiment on next. Tensorflow is a powerful computation engine, very popular in research and academic worlds too. Last week it was noticed that Python overtook Java by becoming the most popular language in PYPL Index. I used a Win10 laptop at hand, but the approach works the same on MacOS and Linux. This "Part I" is a quick record on how to set up a "simple" but popular deep learning demo environment step-by-step with a Python 3 binding to a HealthShare 2017.2.1 instance. Keywords: Anaconda, Jupyter Notebook, Tensorflow GPU, Deep Learning, Python 3 and HealthShare 1.
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