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First-Order Stochastic Optimization

Offered By: Simons Institute via YouTube

Tags

Stochastic Optimization Courses Deep Learning Courses Gradient Descent Courses

Course Description

Overview

Explore first-order stochastic optimization techniques in this 58-minute lecture by Rachel Ward from the University of Texas at Austin, presented at the Foundations of Data Science Boot Camp. Delve into support vector machines, deep learning, and gradient descent methods. Examine theorems, proofs, and important sampling techniques. Analyze convergence rates, adaptive learning strategies, and dynamic updates in optimization algorithms. Gain insights into the mathematical foundations underpinning modern data science and machine learning approaches.

Syllabus

Introduction
Support Vector Machine
Deep Learning
Gradient Descent
Questions
Theorem
Proof
Important Sampling
Convergence Rate
Adaptive Learning
Dynamic Updates


Taught by

Simons Institute

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