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A deep learning model does not train directly from files. It trains from tensors. The purpose of a data pipeline is to convert stored data into batches of tensors with consistent shapes, data types, and labels.
Linear regression predicts a real number. Logistic regression predicts a probability for binary classification.
Softmax regression extends logistic regression from two classes to many classes. It is the standard linear model for multiclass classification.
The perceptron is one of the earliest algorithms for binary classification. It learns a linear decision boundary by updating its weights whenever it makes a mistake.
Linear separability describes when a classification dataset can be divided perfectly by a linear decision boundary. It is one of the central geometric ideas behind linear classification.
A linear classifier separates classes using a hyperplane. In two dimensions this boundary is a line. In three dimensions it is a plane. In higher dimensions it is a hyperplane.
Linear models are the first useful class of predictive models in deep learning.