Artificial Neural Networks

Neural networks (also referred to as connectionist systems) are a computational approach, which is based on a large collection of neural units (AKA artificial neurons), loosely modeling the way a biological brain solves problems with large clusters of biological neurons connected by axons.


History
Hebbian learning
Backpropagation and resurgence
Improvements since 2006
Models
Network function
Learning
Choosing a cost function
Learning paradigms
Supervised learning
Unsupervised learning
Reinforcement learning
Learning algorithms
Applications
Real-life applications
Neural networks and neuroscience
Types of models
Networks with memory
Types of artificial neural networks
Theoretical properties
Computational power
Capacity
Convergence
Generalization and statistics
Criticism