Machine Learning Glossary

From David's Wiki
Revision as of 20:08, 23 September 2021 by David (talk | contribs)
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to navigation Jump to search
\( \newcommand{\P}[]{\unicode{xB6}} \newcommand{\AA}[]{\unicode{x212B}} \newcommand{\empty}[]{\emptyset} \newcommand{\O}[]{\emptyset} \newcommand{\Alpha}[]{Α} \newcommand{\Beta}[]{Β} \newcommand{\Epsilon}[]{Ε} \newcommand{\Iota}[]{Ι} \newcommand{\Kappa}[]{Κ} \newcommand{\Rho}[]{Ρ} \newcommand{\Tau}[]{Τ} \newcommand{\Zeta}[]{Ζ} \newcommand{\Mu}[]{\unicode{x039C}} \newcommand{\Chi}[]{Χ} \newcommand{\Eta}[]{\unicode{x0397}} \newcommand{\Nu}[]{\unicode{x039D}} \newcommand{\Omicron}[]{\unicode{x039F}} \DeclareMathOperator{\sgn}{sgn} \def\oiint{\mathop{\vcenter{\mathchoice{\huge\unicode{x222F}\,}{\unicode{x222F}}{\unicode{x222F}}{\unicode{x222F}}}\,}\nolimits} \def\oiiint{\mathop{\vcenter{\mathchoice{\huge\unicode{x2230}\,}{\unicode{x2230}}{\unicode{x2230}}{\unicode{x2230}}}\,}\nolimits} \)

Machine Learning, Computer Vision, and Computer Graphics Glossary

C

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.

G

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.

T

U

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