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e-Seminar on Scientific Machine Learning Speaker: Dr. Jan Drgona (PNNL) Abstract: In this talk, we will present a Deep learning has led to encouraging successes in many challenging tasks. However, a deep neural In Fall 2020 and Spring 2021, this was MIT's 18.337J/6.338J: Parallel Computing and Scientific Machine Learning course. Scientific computing is increasingly incorporating the advancements in machine learning and the ability to work with large ... Talk from HSF/IRIS-HEP Analysis Ecosystem 2 Workshop ( For more information about Stanford's Artificial Intelligence professional and graduate programs visit:

Boeing Distinguished Colloquium, November 21, 2019 Alan Edelman Massachusetts Institute of Technology Title: Julia: ... Jan Drgona, Pacific Northwest National Laboratory July 10, 2024 Fourth Symposium on Machine Learning and Dynamical ... Chris Rackauckas (MIT), "Generalized Physics-Informed Learning through Language-Wide Behind Every Great Deep Learning Framework Is An Even Greater Today we're joined by Patrick Heimbach, a professor at the University of Texas working at the intersection of ML and ... This tutorial will cover how to optimise various aspects of analyses -- such as cuts, binning, and learned observables like neural ...

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Models as Code Differentiable Programming with Julia by Viral Shah This video was recorded at Scala Days Berlin 2018 Follow us on or visit our website for more information ...

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What is Differentiable Programming
Machine Learning 10 - Differentiable Programming | Stanford CS221: AI (Autumn 2021)
Boeing Colloquium: Julia: Differentiable Programming and Software 2.0
Differentiable Programming for Data-driven Modeling, Optimization, and Control
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Differentiable Programming (Part 1)
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Last Updated: June 12, 2026

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