The Model Asset Exchange on IBM Developer is a place to find and use free, open source, state-of-the-art deep learning models for common application domains, such as text, image, audio, and video processing.
Patrick Titzler, va barbosa, and Jeremy Nilmeier demonstrate how to incorporate state-of-the-art open source deep learning functionality into your applications and services and how to train a model using your own data.
This event is sponsored by IBM.
va barbosa is a developer advocate at the Center for Open-Source Data & AI Technologies at IBM, where he helps developers discover and use data and machine learning technologies. This is fueled by his passion to help others and guided by his enthusiasm for open source technology. Always looking to embrace new challenges and fulfill his appetite for learning, he immerses himself in a wide range of technologies and activities. When not focusing on the developer experience, he enjoys dabbling in photography. If you can’t find him in front of a computer, try looking behind a camera.
Patrick Titzler is a developer advocate with the Center for Open-Source Data & AI Technologies (CODAIT) at IBM. Combining his enterprise application development experience with his interest in data science and artificial intelligence, he focuses on making it easier for developers to incorporate deep learning into their applications. His latest project is a public exchange for ready-to-use open source model assets.
Jeremy Nilmeier is a developer advocate, data scientist, and member of the IBM Center for Open-Source Data & AI Technologies (CODAIT), where he works with with open source frameworks for big data, machine learning, and deep learning. He completed the Insight data engineering program. He holds a BS in chemical engineering from the University of California, Berkeley, and a PhD in computational biophysics from the University of California, San Diego, which he followed with postdoctoral research in biophysics and bioinformatics at UC Berkeley, Lawrence Berkeley and Livermore Laboratories, and Stanford as an OpenMM fellow.
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