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Linear regression is the simplest supervised learning model used in deep learning.
Logistic regression is a linear model for classification. It predicts a probability instead of a raw numerical value. Despite its name, logistic regression is mainly used for classification, not regression.
A loss function measures how wrong a model’s predictions are.
Gradient descent is the basic optimization method used to train neural networks. It updates model parameters in the direction that reduces the loss.
Stochastic gradient descent, usually abbreviated as SGD, is the standard form of gradient-based training used in deep learning.
Stochastic gradient descent uses the current minibatch gradient to update the parameters.
The learning rate controls the size of each parameter update.
Training loss measures how well a model fits the training data.