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.
When it comes to using large language models, there are effective approaches — and less effective ones. Here are some tips to get the answers you need from an LLM.
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.
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.
