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Gradient Descent and Stochastic Gradient Descent

Paul Hand via YouTube

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This lecture analyzes gradient descent and stochastic gradient descent for training neural networks, emphasizing convergence rates, convexity assumptions, learning-rate selection, and nonconvex optimization challenges.

Syllabus

Introduction
Gradient Descent Convergence
Recovery Theorem
Proof
Interpretation
Gradient Descent Challenges
Stochastic Gradient Descent
Step sizes and learning rates
Challenges
Learning Rates

Taught by

Paul Hand

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