Neural networks under SGD

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Related lectures (30)

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Neural Networks: Training and Optimization

Explores the training and optimization of neural networks, addressing challenges like non-convex loss functions and local minima.

Gradient Descent: Optimization Techniques

Explores gradient descent, loss functions, and optimization techniques in neural network training.

Generalization in Deep Learning

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

Feed-forward Networks

Introduces feed-forward networks, covering neural network structure, training, activation functions, and optimization, with applications in forecasting and finance.

Multilayer Neural Networks: Deep Learning

Covers the fundamentals of multilayer neural networks and deep learning.

Kinetic Theory: Distribution Function Evolution

Explores the evolution of the distribution function describing particles in phase space.

Stochastic Gradient Descent

Explores stochastic gradient descent optimization and the Mean-Field Method in neural networks.

Double Descent Curves: Overparametrization

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

Risk Management: Quantitative Methods

Explores risk management concepts, including VaR, ES, and measurement methods.

Quantitative Risk Management: Risk Measures

Covers risk measures used for determining risk capital and capital adequacy.