Unlocking the power of machine learning for your JavaScript applications with TensorFlow

Kangyi Zhang is a software engineer at Google Brain and a member of the TensorFlow.js team. He’s very excited about sharing how to do machine learning in the JavaScript world, concentrating on native TensorFlow execution under the Node.js runtime, and preparing data for machine learning model in JS. You can find him on GitHub @kangyizhang.

Brijesh Krishnaswami is a technical program manager on the TensorFlow team at Google. He has a master’s degree in computer science and two decades of experience in software development at various technology companies. You can find him on LinkedIn.

Joseph Paul Cohen is a postdoctoral fellow with Yoshua Bengio at Mila and the University of Montreal. Joseph leads the medical research group at Mila, focusing on computer vision, genomics, and clinical data. He holds a PhD in computer science and machine learning from the University of Massachusetts Boston. His research interests include healthcare, bioinformatics, machine learning, computer vision, ad hoc networking, and cybersecurity. Joseph received a US National Science Foundation Graduate Fellowship as well as an IVADO Postdoctoral Fellowship. He’s the director of the Institute for Reproducible Research, which is dedicated to improving the process of scientific research using technology.

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Brendan Duke is a machine learning researcher at ModiFace, where he worked on Nail Polish Try-On, acne and skin analysis, and on optimized conversion and deployment of research models to production hardware and software backends. He earned a master’s degree under the supervision of Graham Taylor at the University of Guelph, where he worked on machine learning for human activity recognition focused on multimodal interactions.