Examples applications of Oja's learning rule

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

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

Explores the statistical mechanics of learning, focusing on neural networks' mysteries and computational challenges.

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

Explores neural network learning dynamics, covering optimization, interference, and continual learning challenges.