## From Linear Regression to Linear Classification

Preliminaries An Introduction to Linear Regression A Simple Linear Regression Bayesian theorem Feature extraction Recall Linear Regression The goal of a regression problem is to find out a function or hypothesis that given an input $$\mathbf{x}$$, it can make a prediction $$\hat{y}$$ to estimate the target. Both the target $$y$$ and prediction $$\hat{y}$$ here are continuous. They have the properties of numbers1: Consider 3 inputs $$\mathbf{x}_1$$, $$\mathbf{x}_2$$ and $$\mathbf{x}_3$$ and their coresponding targets are $$y_1=0$$, $$y_2=1$$ and $$y_3=2$$....

February 17, 2020 · (Last Modification: April 28, 2022) · Anthony Tan

## Neuron Model and Network Architecture

Preliminaries linear classifier An Introduction to Neural Networks Theory and Notation1 We are not able to build any artificial cells up to now. It seems impossible to build a neuron network through biological materials manually, either. To investigate the ability of neurons we have built mathematical models of the neuron. These models have been assigned a number of neuron-like properties. However, there must be a balance between the number of properties contained by the mathematical models and the current computational abilities of the machines....

December 10, 2019 · (Last Modification: April 29, 2022) · Anthony Tan