Machine learning (ML)
A mathematical model that is able to improve its performance on a task by exposure to data.
Deep neural networks
ML models with one or more latent (hidden) layers allowing for the generation of non-linear output and complex interactions between layers. Deep neural networks power “deep learning,” which enables tasks, such as image recognition, natural language processing (NLP), and complex predictions.
Subtypes of deep neural networks are classified based on the relationship between hidden layers and include convolutional, recurrent, gated graph, and generative adversarial neural networks.
Training, test, and validation sets
Training set: Dataset from which the model learns the optimal parameters to accomplish the task.
Test set: Dataset on which the performance of a trained, parameterized model is evaluated.
Validation set: Dataset that is used to evaluate the model’s performance during training. Differs from a test set in that it is used during training to establish hyperparameters of the model.
A subset of ML in which the outcomes to be learned by the model (“labels”) are provided in the training set. For example, teaching a model to identify breast cancer patients for study inclusion would require training the model on a training set containing labeled patients with and without breast cancer prior to validating that model on a new set of unlabeled patients with and without breast cancer.
A subset of ML in which there are no pre-specified labels for the model to learn to predict; instead, models identify hidden patterns in the data.
Natural language processing (NLP)
A form of artificial intelligence that enables the understanding of language. Much modern NLP uses deep neural networks in which words and their relationships to each other are encoded in a set of highly dimensional vectors, enabling the model to parse the meaning of new pieces of text it is presented with.