Differentiable Programming for End-to-end Optimization of Experiments - Lecture 6
Offered By: International Centre for Theoretical Sciences via YouTube
Course Description
Overview
Explore differentiable programming for end-to-end optimization of experiments in this lecture by Tommaso Dorigo, part of the "Statistical Methods and Machine Learning in High Energy Physics" program. Delve into advanced techniques for analyzing large-scale data in high energy physics research, with a focus on machine learning applications. Gain insights into the future of data-driven approaches in particle physics, including strategies for processing petabytes of data from the Large Hadron Collider and other precision experiments. Learn how deep machine learning and artificial intelligence frameworks are revolutionizing the search for new physics and the precise measurement of Higgs boson properties. Suitable for PhD students, postdoctoral researchers, and professionals in theoretical or experimental particle physics and astro-particle physics with programming experience in Python and C++, as well as familiarity with event generation and data analysis tools.
Syllabus
Differentiable Programming for End-to-end Optimization of Experiments (Lecture 6) by Tommaso Dorigo
Taught by
International Centre for Theoretical Sciences
Related Courses
Deep Learning Fundamentals with KerasIBM via edX Deep Learning Essentials
Université de Montréal via edX Deep Learning with TensorFlow 2.0
Udemy Data Science: Deep Learning and Neural Networks in Python
Udemy Нейронные сети и глубокое обучение
DeepLearning.AI via Coursera