Generalization in Deep Learning

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Related lectures (32)

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Double Descent Curves: Overparametrization

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

Generalization in Deep Learning

Delves into the trade-off between model complexity and risk, generalization bounds, and the dangers of overfitting complex function classes.

Implicit Bias in Machine Learning

Explores implicit bias, gradient descent, stability in optimization algorithms, and generalization bounds in machine learning.

Complexity: Approximation-Estimation Trade-off

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

Learning with Deep Neural Networks

Explores the success and challenges of deep learning, including overfitting, generalization, and the impact on various domains.

Neural Networks: Two Layers Neural Network

Covers the basics of neural networks, focusing on the development from two layers neural networks to deep neural networks.

Gradient Descent on Two-Layer ReLU Neural Networks

Analyzes gradient descent on two-layer ReLU neural networks, exploring global convergence, regularization, implicit bias, and statistical efficiency.

Understanding Generalization: Implicit Bias & Optimization

Explores the trade-off between complexity and risk in machine learning models, the benefits of overparametrization, and the implicit bias of optimization algorithms.

Deep Learning: Theory and Practice

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

Numerical analysis

Covers advanced numerical analysis topics including deep neural networks and optimization methods.