Stochastic Gradient Descent Methods with Biased Estimators
Offered By: VinAI via YouTube
Course Description
Overview
Explore a comprehensive seminar on stochastic gradient descent methods using biased estimators, presented by Quoc Tran-Dinh from the University of North Carolina at Chapel Hill. Delve into recent advancements in gradient descent algorithms, their variants, and practical applications in machine learning. Gain insights into the speaker's research on stochastic gradient-based methods for large-scale optimization and minimax problems, with potential applications in deep learning, statistical learning, generative adversarial nets, and federated learning. Learn about the collaborative work with researchers from UNC and IBM, and understand the theoretical and practical aspects of these cutting-edge optimization techniques.
Syllabus
[Seminar Series] Stochastic Gradient Descent Methods with Biased Estimators
Taught by
VinAI
Related Courses
Neural Networks for Machine LearningUniversity of Toronto via Coursera 機器學習技法 (Machine Learning Techniques)
National Taiwan University via Coursera Machine Learning Capstone: An Intelligent Application with Deep Learning
University of Washington via Coursera Прикладные задачи анализа данных
Moscow Institute of Physics and Technology via Coursera Leading Ambitious Teaching and Learning
Microsoft via edX