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Machine Learning Glossary: Difference between revisions
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* Deep Learning - The use of neural networks (i.e. >= 2 layers) in machine learning tasks. | * Deep Learning - The use of neural networks (i.e. >= 2 layers) in machine learning tasks. | ||
* Dilation - Spacing between elements in a CNN kernel when applied. See [[Convolutional neural network]]. | * Dilation - Spacing between elements in a CNN kernel when applied. See [[Convolutional neural network]]. | ||
* [[Diffusion Models]] - A method which iteratively applies a neural network to sample from a target distribution. | |||
* Domain Adaptation - An area of research focused on making neural network work with alternate domains, or sources of data. | * Domain Adaptation - An area of research focused on making neural network work with alternate domains, or sources of data. | ||
* Dropout - A technique where you zero out the features outputs of a random percent of neurons in each iteration, turning your network into an ensemble of subnetworks during training. | * Dropout - A technique where you zero out the features outputs of a random percent of neurons in each iteration, turning your network into an ensemble of subnetworks during training. | ||