Augmentation
Augmentation is a process of artificial data generation, which produces a greater volume of data, and thus increasing the likelihood of obtaining higher predictive accuracy of a predictive model.
Usually, a higher volume of data is likely to yield better predictive and more accurate models from training as the algorithm is able to see a greater variety of examples. However, it is not always possible to collect a large amount of data, hence augmentation is required to generate sufficient data to train an accurate predictive model. This is particularly relevant for datasets with images. There are many methods of generating new training examples with images. These include:
 mirroring the image
 adding noise to the image
 distorting the image
Augmentation creates augmented data. Augmented data is based on systematic modification of existing data (with images often through simple linear algebra operations on the whole image) as opposed to synthetic data.
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Artificial intelligence
 artificial intelligence (AI)
 imaging data sets
 computeraided diagnosis (CAD)
 natural language processing
 machine learning (overview)
 visualizing and understanding neural networks
 common data preparation/preprocessing steps
 DICOM to bitmap conversion
 dimensionality reduction
 scaling
 centring
 normalization
 principal component analysis
 training, testing and validation datasets
 augmentation
 loss function

optimization algorithms
 ADAM
 momentum (Nesterov)
 stochastic gradient descent
 minibatch gradient descent

regularisation
 linear and quadratic
 batch normalization
 ensembling
 rulebased expert systems
 glossary
 activation function
 anomaly detection
 automation bias
 backpropagation
 batch size
 cost function
 confusion matrix
 convolution
 curse of dimensionality
 epoch
 gradient descent
 iteration
 linear algebra
 noise reduction
 R (Programming Language)
 Python (Programming Language)
 synthetic and augmented data
 overfitting
 transfer learning