GitHub - mitliagkas/theoretical-principles-for-deep-learning-UdeM-Mila-notes: Scribed notes for the [IFT6085] Deep Learning Theory course at UdeM

Name		Name	Last commit message	Last commit date
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Lecture 02 - Basics of convex analysis and gradient descent		Lecture 02 - Basics of convex analysis and gradient descent
Lecture 03 - Gradient descent for smooth and for strongly convex functions		Lecture 03 - Gradient descent for smooth and for strongly convex functions
Lecture 04 - Black-box Models and Lower bounds		Lecture 04 - Black-box Models and Lower bounds
Lecture 05 - Accelerated methods, Polyak momentum		Lecture 05 - Accelerated methods, Polyak momentum
Lecture 06 - Nesterov's Accelerated Gradient, SGD		Lecture 06 - Nesterov's Accelerated Gradient, SGD
Lecture 07 - Statistical learning theory		Lecture 07 - Statistical learning theory
Lecture 08 - PAC-Bayes bounds		Lecture 08 - PAC-Bayes bounds
Lecture 09 - Stability and Generalization		Lecture 09 - Stability and Generalization
Lecture 11 - Expressivity and universal approximation		Lecture 11 - Expressivity and universal approximation
Lecture 12 - Random kitchen sinks		Lecture 12 - Random kitchen sinks
Lecture 14 - The Numerics of GANs		Lecture 14 - The Numerics of GANs
Lecture 19 - RL		Lecture 19 - RL
Refs		Refs
utils		utils