Statistical Physics of Deep Learning

Related lectures (29)

Page 3 of 3

Explores data representations, histograms, neural networks, and deep learning concepts.

Covers the fundamentals of deep learning, from artificial neurons to modern networks.

Covers a mathematical introduction to deep learning, including challenges, power of linear classifiers, model scaling, and theoretical aspects.

Explores artificial neural networks, reward information in the brain, animal conditioning, deep reinforcement learning, and a quiz on rewards.

Covers the back-propagation algorithm for deep neural networks and the importance of locality in CNN.

Explores the basics of neural networks, with a focus on deep neural networks and their architecture and training.

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

Delves into the challenges of deep learning, exploring dimensionality, performance, and overfitting phenomena in neural networks.

Explores deep learning for question answering, analyzing neural networks and model robustness to noise.