Neural Networks: Supervised Learning and Backpropagation

Related lectures (31)

Page 3 of 4

Delves into neural network approximation, supervised learning, challenges in high-dimensional learning, and deep learning experimental revolution.

Explores supervised learning as an ill-posed problem and the integration of sparse adaptive splines into neural architectures.

Covers key concepts in reinforcement learning, neural networks, clustering, and unsupervised learning, emphasizing their applications and challenges.

Explores computational models of the ventral visual system, focusing on optimizing networks for real-world tasks and comparing to brain data.

Covers the derivation of the stochastic gradient descent formula for a simple perceptron and explores the geometric interpretation of classification.

Explores language detection using Recurrent Neural Networks and supervised learning concepts.

Offers insights into the statistical physics of learning, exploring the relationship between neural network structure and disordered systems.

Discusses challenges in building physical neural networks, focusing on depth, connections, and trainability.

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

Introduces supervised learning using labeled data points to optimize classifier output.