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glossary [2022/09/12 13:44] – adminglossary [2022/09/12 16:51] (current) – admin
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 ==== convolutional layer ==== ==== convolutional layer ====
  
-A layer in a [[:glossary#deep neural network|deep neural network]] in which a convolutional filter passes over the input matrix.+A layer in a [[:glossary#deep_neural_network|deep neural network]] in which a convolutional filter passes over the input matrix.
  
 ==== convolutional neural network (CNN) ==== ==== convolutional neural network (CNN) ====
 +
 +A neural network in which at least one layer is a [[:glossary#convolutional_layer|convolutional layer]].
  
 ==== cross-validation ==== ==== cross-validation ====
 +
 +A method to estimate how well a model will generalise to new data. In cross-validation, the model is trained on a subset of the data and then validated on the remaining non-overlapping subsets, e.g. [[:glossary#k-fold_cross-validation|k-fold cross-validation]].
  
 ===== D ===== ===== D =====
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 ==== feature engineering ==== ==== feature engineering ====
  
-The process of converting data into useful [[:glossary#feature|features]] for training a model. [[:glossary#feature_selection|Feature selection]] is a part of feature engineering.+The process of converting data into useful [[:glossary#feature|features]] for training a model.
  
 ==== feature selection ==== ==== feature selection ====
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 ===== K ===== ===== K =====
  
-==== k-fold validation ====+==== k-fold cross-validation ==== 
 + 
 +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.1662983044.txt.gz · Last modified: 2022/09/12 13:44 by admin