Explains the learning process in multi-layer neural networks, including back-propagation, activation functions, weights update, and error backpropagation.

Neural Networks and Deep Learning

Covers formal neuron models, activation functions, and approximation results for neural networks.

Storage Capacity: Prototypes and Neuronal Dynamics

Explores the storage capacity of associative memory in networks of neurons and the impact of multiple prototypes on error rates.

Understanding Machine Learning: Exactly Solvable Models

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

Multi-layer Neural Networks

Covers the fundamentals of multi-layer neural networks and the training process of fully connected networks with hidden layers.

Ring Attractor Dynamics in Drosophila Brain

Explores ring attractor networks in the Drosophila brain for maintaining heading direction during navigation.