glossary
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glossary [2022/09/12 13:17] – admin | glossary [2022/09/12 16:51] (current) – admin | ||
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Grouping of data, particulary during [[: | Grouping of data, particulary during [[: | ||
+ | |||
+ | ==== convolutional layer ==== | ||
+ | |||
+ | A layer in a [[: | ||
==== convolutional neural network (CNN) ==== | ==== convolutional neural network (CNN) ==== | ||
+ | |||
+ | A neural network in which at least one layer is a [[: | ||
==== cross-validation ==== | ==== cross-validation ==== | ||
+ | |||
+ | A method to estimate how well a model will generalise to new data. In cross-validation, | ||
===== D ===== | ===== D ===== | ||
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==== data imbalance ==== | ==== data imbalance ==== | ||
- | When the [[: | + | When the [[: |
==== deep learning ==== | ==== deep learning ==== | ||
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==== feature engineering ==== | ==== feature engineering ==== | ||
- | The process of converting data into useful [[: | + | The process of converting data into useful [[: |
==== feature selection ==== | ==== feature selection ==== | ||
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==== hidden layer ==== | ==== hidden layer ==== | ||
+ | |||
+ | Artificial layer in a [[: | ||
==== hierarchical agglomerative clustering ==== | ==== hierarchical agglomerative clustering ==== | ||
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==== hyperparameters ==== | ==== hyperparameters ==== | ||
+ | |||
+ | Higher-level properties of a model, such as the learning rate (how fast it can learn) or the number of [[: | ||
===== I ===== | ===== I ===== | ||
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===== K ===== | ===== K ===== | ||
- | ==== k-fold validation ==== | + | ==== k-fold |
+ | |||
+ | The training set is split into k smaller subsets. The model is trained on one of the k folds as training set and validated on the remaining (k-1) folds. This is done for all k folds. The performance measure calculated by the k-fold cross-validation is the average of the results of all k folds. | ||
===== L ===== | ===== L ===== |
glossary.1662981467.txt.gz · Last modified: 2022/09/12 13:17 by admin