# Machine Learning Glossary


Machine Learning, Computer Vision, and Computer Graphics Glossary

## D

• Dilation - how spread out a CNN kernel is. See Convolutional neural network.
• Domain Adaptation - An area of research focused on making neural network work with alternate domains, or sources of data.

## E

• Early stopping - a technique where you stop training once the validation loss begins increasing. This is not used very often anymore with large models.

## F

• Fully connected network - The standard neural network model where each layer is a sequence of nodes.

## I

• Intersection over Union - A metric for computing the accuracy of bounding box prediction.

## L

• Long short-term memory or LSTM - An RNN neural network architecture which has two sets of hidden states for long and short term.

## M

• Multilayer perceptron - See Fully connected network.

## N

• Normalized Device Coordinates - In images, pixels are in coordinates of $$\displaystyle [-1, 1]\times[-1, 1]$$.

## O

• Overfitting - when a model begins to learn noise specific to your training data, thereby worsening performance on non-training data.

## R

• Recurrent neural network (RNN) - A type of neural network which operates sequentially on sequence data.
• Reinforcement learning - an area of machine learning focused on learning to perform actions, E.g. playing a game

## S

• Stride - how far the CNN kernel in terms of input pixels moves between output pixels.

## U

• Underfitting - when a model performs poorly on both training and validation data, usually due to inadequate model complexity or training duration.