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In tandem, the last few years have seen a proliferation of cutting-edge, commercially usable machine learning frameworks, including the highly successful scikit-learn Python library and well-publicized releases of libraries like Tensorflow by Google and CNTK by Microsoft Research.
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Compared with KNIME, the SciLuigi solution presented here provides three additions of vital importance for machine learning workflows in drug discovery: HPC support, ability to create a library of custom, re-usable components and detailed audit trails.
While projects like TensorFlow make it easier to build machine learning algorithms and train them for certain types of data inputs, TensorFlow Serving specializes in making these models usable in production environments.
This thesis examines machine learning through the lens of human-computer interaction in order to address fundamental questions surrounding the application of machine learning to real-life problems, including: Can we make machine learning algorithms more usable and useful?
That's machine learning.
Machine Learning (JMLR, 2002).
Could machine learning help?
Automated machine learning.
Large scale machine learning.
Jongho Kim – machine learning.
Machine Learning program, CAREER Awards.
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