Choosing The Right Deep Learning Framework: A Deep Learning Approach
Choosing The Right Deep Learning Framework: A Deep Learning Approach


As a developer advocate for IBM, I use machine learning in order to understand machine learning developers. I spend time building models to identify the problems they solve and the tools that they use to do so. This talk will present how deep learning can be used to predict the deep learning framework that most closely resembles the style in which a machine learning developer programs. Many of these frameworks are very new (for instance TensorFlow is in the process of putting out just its second major release). As the field of deep learning and the frameworks enabling them continue to rapidly change, the community using a particular one will be much more consistent. This talk will end by allowing developers to test them models themselves by uploading examples of their work via Jupyter Notebooks and predict the deep learning community and framework that is right for them.


Before becoming an AI Advocate at IBM, Nick studied computer science at Purdue University and the University of Southern California, and was a high performance computing consultant for Hewlett-Packard in Grenoble, France. He now specializes in machine learning and utilizes it to understand machine learning developers of various communities, startups, and enterprises in order to help them succeed on IBM's data science platform. He has a strong interest in data science education and open source software.

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