Generalization in Deep Learning

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Provides insights on gradient-based algorithms, deep learning mysteries, and the challenges of non-convex problems.

Covers state space models and the expressivity of transformers in sequence copying tasks.

Explores adaptive gradient methods, their properties, convergence, and comparison with traditional optimization algorithms.

Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.

Introduces the fundamentals of statistical learning, covering supervised learning, decision theory, risk minimization, and overfitting.

Discusses decision trees and random forests, focusing on their structure, optimization, and application in regression and classification tasks.

Explores the deep learning paradigm, including challenges, neural networks, robustness, fairness, interpretability, and energy efficiency.

By Prof. Volkan Cevher delves into the mathematics of deep learning, exploring model complexity, risk trade-offs, and the generalization mystery.

Explores deep learning fundamentals, including image classification, neural network working principles, and machine learning challenges.

Explores conclusions from statistical learning theory, emphasizing function complexity, generalization, and the bias-variance trade-off.