	A	C	D	E	F	G	H	J	L	N	O	P	Q	R	T	U	V	W	X	Z	AA	AC	AD
1	Concept	Context	Preferred Label	Alternate Label	Abbreviation	Synonym	Synonym Context	Identifier	Definition (text)	Concept Source	Preferred Label Source	Alternate Label Source	Definition Source (isDefinedBy)	Definition Adapted from Source	Scope Note	Explanatory Note	Usage Note	Example	Dependencies	Parent Concept	Equivalent Concept	Concept/Definition Usage (Literature)	References

2	Actvity		actvity					actvity	something that occurs over a period of time and acts upon or with entities	PROV-O: The PROV Ontology. [Online]. Available: https://www.w3.org/TR/prov-o/#Activity. [Accessed: 02-Nov-2018].													PROV-O: The PROV Ontology. [Online]. Available: https://www.w3.org/TR/prov-o/#Activity. [Accessed: 02-Nov-2018].
3	Convolutional Layer		convolutional layer					convolutional_layer	performs the convolution operation using a learned filter	F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 815–823.													D. Cornelisse, “An intuitive guide to Convolutional Neural Networks,” freeCodeCamp.org, 24-Apr-2018. [Online]. Available: https://medium.freecodecamp.org/an-intuitive-guide-to-convolutional-neural-networks-260c2de0a050. [Accessed: 18-Oct-2018].
4	Convolutional Neural Network		convolutional neural network		CNN			convolutional_neural_network	a neural network that has one or more layers of learned weighted filters	F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 815–823.						CNN are the model variants achieving the highest accuracies in most image based machine learning tasks		FaceNet					CS231n Convolutional Neural Networks for Visual Recognition. [Online]. Available: http://cs231n.github.io/convolutional-networks/. [Accessed: 06-Oct-2018].
5	Data Sample		data sample					data_sample	basic unit of data consumed by a machine learning model containing data features and a groundtruth label	G. B. Huang, M. Mattar, T. Berg, and E. Learned-Miller, “Labeled faces in the wild: A database for studying face recognition in unconstrained environments,” in Workshop on faces in’Real-Life’Images: detection, alignment, and recognition, 2008.								Image with groundtruth tag					10153068920222172, “About Train, Validation and Test Sets in Machine Learning,” Towards Data Science, 06-Dec-2017. [Online]. Available: https://towardsdatascience.com/train-validation-and-test-sets-72cb40cba9e7. [Accessed: 06-Oct-2018].
6	Data Set		data set						collection of data samples used to train, validate, and test a machine learning model	G. B. Huang, M. Mattar, T. Berg, and E. Learned-Miller, “Labeled faces in the wild: A database for studying face recognition in unconstrained environments,” in Workshop on faces in’Real-Life’Images: detection, alignment, and recognition, 2008.													10153068920222172, “About Train, Validation and Test Sets in Machine Learning,” Towards Data Science, 06-Dec-2017. [Online]. Available: https://towardsdatascience.com/train-validation-and-test-sets-72cb40cba9e7. [Accessed: 06-Oct-2018].
7	Engineered Features		engineered features					engineered_features	features manually developed by a programmer	F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 815–823.						Kumars features we be considered engineered features becuase they were manually chosen instead of learned throguh a ML technique							F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 815–823.
8	Fully Connected Layer		fully connected layer					fully_connected_layer	summation across the products of the learned bias and the previous layer passed through an activation function	F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 815–823.													D. Cornelisse, “An intuitive guide to Convolutional Neural Networks,” freeCodeCamp.org, 24-Apr-2018. [Online]. Available: https://medium.freecodecamp.org/an-intuitive-guide-to-convolutional-neural-networks-260c2de0a050. [Accessed: 18-Oct-2018].
9	hasActivationFunction		has activation function					has_activation_function	links a fully connected layer to the activation function used by the neurons	F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 815–823.													D. Cornelisse, “An intuitive guide to Convolutional Neural Networks,” freeCodeCamp.org, 24-Apr-2018. [Online]. Available: https://medium.freecodecamp.org/an-intuitive-guide-to-convolutional-neural-networks-260c2de0a050. [Accessed: 18-Oct-2018].
10	hasDimension		has dimension					has_dimension	the number of functions with a layer	F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 815–823.													D. Cornelisse, “An intuitive guide to Convolutional Neural Networks,” freeCodeCamp.org, 24-Apr-2018. [Online]. Available: https://medium.freecodecamp.org/an-intuitive-guide-to-convolutional-neural-networks-260c2de0a050. [Accessed: 18-Oct-2018].
11	hasFeature		hasfeature					hasfeature	links an entity to the data characteristics	F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 815–823.													F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 815–823.
12	hasIndex		has index					has_index	the position of the layer in the model	F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 815–823.													D. Cornelisse, “An intuitive guide to Convolutional Neural Networks,” freeCodeCamp.org, 24-Apr-2018. [Online]. Available: https://medium.freecodecamp.org/an-intuitive-guide-to-convolutional-neural-networks-260c2de0a050. [Accessed: 18-Oct-2018].
13	hasMatrixSizeX		has matrix size x					has_matrix_size_x	the number of columns in a matrix	F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 815–823.													D. Cornelisse, “An intuitive guide to Convolutional Neural Networks,” freeCodeCamp.org, 24-Apr-2018. [Online]. Available: https://medium.freecodecamp.org/an-intuitive-guide-to-convolutional-neural-networks-260c2de0a050. [Accessed: 18-Oct-2018].
14	hasMatrixSizeY		has matrix size x					has_matrix_size_x	the number of rows in a matrix	F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 815–823.													D. Cornelisse, “An intuitive guide to Convolutional Neural Networks,” freeCodeCamp.org, 24-Apr-2018. [Online]. Available: https://medium.freecodecamp.org/an-intuitive-guide-to-convolutional-neural-networks-260c2de0a050. [Accessed: 18-Oct-2018].
15	hasPoolingFunction		has pooling function					has_pooling_function	links a pooling layer to the pooling function used by the neurons	F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 815–823.													D. Cornelisse, “An intuitive guide to Convolutional Neural Networks,” freeCodeCamp.org, 24-Apr-2018. [Online]. Available: https://medium.freecodecamp.org/an-intuitive-guide-to-convolutional-neural-networks-260c2de0a050. [Accessed: 18-Oct-2018].
16	Inception Layer		inception layer					inception_layer	perfroms multiple convolutions and pooling operations in parallel	F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 815–823.													F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 815–823.
17	Layer		layer					layer	collection of functions at the same depth within a neural network	F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 815–823.													Neural Networks. [Online]. Available: https://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html#What is a Neural Network. [Accessed: 18-Oct-2018].
18	Model		model							F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 815–823.													“Training ML Models,” Amazon. [Online]. Available: https://docs.aws.amazon.com/machine-learning/latest/dg/training-ml-models.html. [Accessed: 18-Oct-2018].
19	Model Accuracy		model accuracy					model_accuracy	percentage of correct results when running the model on a test data set	G. B. Huang, M. Mattar, T. Berg, and E. Learned-Miller, “Labeled faces in the wild: A database for studying face recognition in unconstrained environments,” in Workshop on faces in’Real-Life’Images: detection, alignment, and recognition, 2008.
20	Neural Network		neural network					neural_network	machine learning model that is composed of one or more neural layers where each layer is fully connected to the previous layer	G. B. Huang, M. Mattar, T. Berg, and E. Learned-Miller, “Labeled faces in the wild: A database for studying face recognition in unconstrained environments,” in Workshop on faces in’Real-Life’Images: detection, alignment, and recognition, 2008.													Neural Networks. [Online]. Available: https://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html#What is a Neural Network. [Accessed: 18-Oct-2018].
21	Pooling Layer		pooling layer					pooling_layer	perfroms the max function across the previous layer	F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 815–823.								Max-Pooling Layer					D. Cornelisse, “An intuitive guide to Convolutional Neural Networks,” freeCodeCamp.org, 24-Apr-2018. [Online]. Available: https://medium.freecodecamp.org/an-intuitive-guide-to-convolutional-neural-networks-260c2de0a050. [Accessed: 18-Oct-2018].
22	Result		result					result	a prediction that links data features to a predicted result	G. B. Huang, M. Mattar, T. Berg, and E. Learned-Miller, “Labeled faces in the wild: A database for studying face recognition in unconstrained environments,” in Workshop on faces in’Real-Life’Images: detection, alignment, and recognition, 2008.													“Evaluating ML Models,” Amazon. [Online]. Available: https://docs.aws.amazon.com/machine-learning/latest/dg/evaluating_models.html. [Accessed: 18-Oct-2018].
23	Result Set		result set					result_set	collection of predictions generated by a machine learning model during testing	G. B. Huang, M. Mattar, T. Berg, and E. Learned-Miller, “Labeled faces in the wild: A database for studying face recognition in unconstrained environments,” in Workshop on faces in’Real-Life’Images: detection, alignment, and recognition, 2008.													“Evaluating ML Models,” Amazon. [Online]. Available: https://docs.aws.amazon.com/machine-learning/latest/dg/evaluating_models.html. [Accessed: 18-Oct-2018].
24	Test Set		test set					test_set	data samples used to provide an unbiased evaluation of a final model	F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 815–823.						Part of the dataset used only to fit the model, disjoint with test and validation							10153068920222172, “About Train, Validation and Test Sets in Machine Learning,” Towards Data Science, 06-Dec-2017. [Online]. Available: https://towardsdatascience.com/train-validation-and-test-sets-72cb40cba9e7. [Accessed: 06-Oct-2018].
25	Testing		testing					testing	process that evaluates a machine learning model by calculating the predictive accuracy across a test set	F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 815–823.													“Evaluating ML Models,” Amazon. [Online]. Available: https://docs.aws.amazon.com/machine-learning/latest/dg/evaluating_models.html. [Accessed: 18-Oct-2018].
26	Training		training					training	process that uses a machine learning algorithm to find patterns within a training set and captures them as machine learning model	F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 815–823.													“Training ML Models,” Amazon. [Online]. Available: https://docs.aws.amazon.com/machine-learning/latest/dg/training-ml-models.html. [Accessed: 18-Oct-2018].
27	Training Set		training set					training_set	data samples used to fit the model	F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 815–823.						Part of the dataset used only to evaluate the model, disjoint with training and validation							10153068920222172, “About Train, Validation and Test Sets in Machine Learning,” Towards Data Science, 06-Dec-2017. [Online]. Available: https://towardsdatascience.com/train-validation-and-test-sets-72cb40cba9e7. [Accessed: 06-Oct-2018].
28	Validation Set		validation set					validation_set	data samples used to provide an unbiased evaluation of a model fit while tuning model hyperparameters	F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 815–823.						Part of the dataset used only to tune hyperparameter, disjoint with test, sometimes part of training							10153068920222172, “About Train, Validation and Test Sets in Machine Learning,” Towards Data Science, 06-Dec-2017. [Online]. Available: https://towardsdatascience.com/train-validation-and-test-sets-72cb40cba9e7. [Accessed: 06-Oct-2018].
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