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Related lectures (24)

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Projection Pursuit Regression: Nonlinear Modeling and Interpretability

Explores Projection Pursuit Regression for nonlinear modeling and the trade-offs with interpretability in neural networks.

Deep Learning for Autonomous Vehicles

Explores deep learning for autonomous vehicles, covering perception, action, and social forecasting in the context of sensor technologies and ethical considerations.

Artificial Intelligence in Finance

Delves into the application of artificial intelligence in finance, exploring tools like neural networks and Bayesian techniques, successful use cases in fraud detection and robo-advisors, and the importance of interpretability in machine learning models.

Transformer Networks: Self-Attention

Explains Transformer networks and self-attention layers for mapping inputs and multi-head attention.

The Hidden Convex Optimization Landscape of Deep Neural Networks

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

Deep Learning Paradigm

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

Interpretable Machine Learning: Sparse Decision Trees and Interpretable Neural Networks

Explores the extremes of interpretability in machine learning, focusing on sparse decision trees and interpretable neural networks.

Adversarial Training: Optimization and Applications

Explores adversarial training optimization, practical implementation, interpretability, fairness, Wasserstein distance, and Wasserstein GANs.

Regression Trees and Ensemble Methods in Machine Learning

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Variance Reduction Techniques

Covers variance reduction techniques in optimization, focusing on gradient descent and stochastic gradient descent methods.