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Better Understanding of Non-convex Methods in Machine Learning

Offered By: Paul G. Allen School via YouTube

Tags

Machine Learning Courses Deep Learning Courses Stochastic Gradient Descent Courses Matrix Completion Courses

Course Description

Overview

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Explore the frontiers of non-convex optimization in machine learning through this illuminating lecture by Princeton University's Tengyu Ma. Delve into recent breakthroughs in deep learning and the challenges of analyzing complex, high-dimensional models trained on massive datasets. Discover new algorithmic approaches and analysis tools for non-convex methods, including insights on matrix completion solved by stochastic gradient descent. Examine the landscape of objective functions for linearized recurrent neural networks and residual networks, and learn how over-parameterization and re-parameterization can simplify optimization processes. Gain valuable knowledge about the formal study of non-convex methods and their applications in advancing machine learning techniques.

Syllabus

Allen School Colloquia: Tengyu Ma (Princeton University)


Taught by

Paul G. Allen School

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