Mean normalization is the process of calculating and subtracting the mean for every feature in a machine learning model and is used in feature scaling. Learn how it works.
Trees are non-linear data structures that store data hierarchically and are made up of nodes connected by edges. Here’s how to implement it in Python using bigtree.
The derivative of the sigmoid function is the sigmoid function multiplied by one minus the sigmoid function and is used in backpropagation. Learn how to calculate it.
Hidden Markov models are probabilistic frameworks where the observed data are modeled as a series of outputs generated by one of several internal, hidden states. Learn more.
