Double Descent Curves: Overparametrization

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (31)

Page 1 of 4

Generalization in Deep Learning

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

Generalization in Deep Learning

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

Variance Reduction Techniques

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

Universal Approximation Theorem: MLP

Covers Multi-Layer Perceptrons (MLP) and their application from classification to regression, including the Universal Approximation Theorem and challenges with gradients.

Learning with Deep Neural Networks

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

Implicit Bias in Machine Learning

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

Perception: Data-Driven Approaches

Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.

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.

Feed-forward Networks

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

Gradient-Based Algorithms in High-Dimensional Learning

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