Lecture

Generalization in Deep Learning

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Explores generalization in deep learning, covering model complexity, implicit bias, and the double descent phenomenon.

Explores double descent curves and overparametrization in machine learning models, highlighting the risks and benefits.

Explores overfitting in polynomial regression, emphasizing the importance of generalization in machine learning and statistics.

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Explores the hidden convex optimization landscape of deep neural networks, showcasing the transition from non-convex to convex models.

Explores the control of complexity in hypothesis spaces and the trade-off between approximation and estimation in risk decomposition.

Covers the fundamentals of multilayer neural networks and deep learning.

Explores neural networks optimization, including backpropagation, batch normalization, weight initialization, and hyperparameter search strategies.

Explores polynomial curve fitting, kernel functions, and regularization techniques, emphasizing the importance of model complexity and overfitting.

Covers model complexity, overfitting, and strategies to select appropriate machine learning models.

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.