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Deep Learning and the Evolution of Useful Information
Logistic Regression: keeping things simple
We begin with regression, specifically logistic regression.
This has long been a staple algorithm for statisticians. A typical application begins with data on two kinds of entities, say, patients with benign tumors and malignant tumors. For both these kinds of patients we have data on the same set of variables, say, patient age, size of tumor, and presence of specific genes.
Abhinanda sarkar biography sample paper
The variables are expressed numerically (this will be important later), possibly with yes/no-type encoding. These variables are combined in a linear fashion—i.e., multiplying by coefficients and adding up. This linear combination is then converted to a score by passing it through a very special kind of function which (to be consistent with what’s to come) we will call an activation function.
If the linear combination gets a low score when passed through the activation function, we predict the patient’s tumor