An activation function that outputs the maximum of multiple linear functions, providing universal approximation.
Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.
A computational model inspired by biological neural networks, consisting of interconnected nodes (neurons) organized in layers that process information through weighted connections.
A non-linear function applied to neuron outputs that introduces non-linearity, enabling networks to learn complex patterns.
A collection of data examples used for training, validating, or testing machine learning models.
The examples a model learns its weights from, kept separate from the validation and test data used to check how well it generalizes.
When a model fits its training data too closely, noise included, so it scores well on examples it has seen and poorly on new ones.
A regularization technique that randomly deactivates neurons during training to prevent overfitting and improve generalization.
An activation function that outputs the maximum of multiple linear functions, providing universal approximation.
This concept is essential for understanding neural networks & deep learning and forms a key part of modern AI systems.
Leads to
Nothing yet: a destination in its own right.