[Neural Networks] An Introduction to Neural Networks
[Neural Networks] An Introduction to Neural Networks

Neural Networks are a model of our brain that is built with neurons and it is considered as the source of intelligence. There is almost $10^{11}$ neurons in the human brain and $10^4$ connections of each neuron to other neurons. Some of these brilliant structures were given when we were born. But this is not a decision for anything, such as our IQ, skills, etc. Because some other structures could be established by experience, and this progress is called learning. Learning is considered as the establishment or modification of the connections between neurons.

[Review] A Logical Calculus of the Ideas Immanent in Nervous Activity
[Review] A Logical Calculus of the Ideas Immanent in Nervous Activity

This paper was published in 1943 by Warren S. McCulloch and Walter Pitts.1 It has been considered as the origin of the neural network field. It contains the background knowledge of biological neural networks of that time. Basing on these biological neural network structure details, authors discarded some "unimportant" or "uncomputable" structures and simplified some complicated structures then get an abstracted model that may have a simple function and could be analyzed mathematically.

[Linear Regression] Simple Linear Regression
[Linear Regression] Simple Linear Regression

We have already created a simple linear model in the post "Introduction to Linear Regression". $y=w_1x_1+w_2x_2$ is a linear equation of both $\boldsymbol{x}=[x_1 \; x_2]^T$ and $\boldsymbol{w}=[w_1 \; w_2]^T$. According to the definition of linear, we come up with the first simplest linear regression:

[Linear Regression] Introduction to Linear Regression
[Linear Regression] Introduction to Linear Regression

Linear regression is a basic idea in statistical or machine learning, especially in supervises learning. The linear regression is a statistical model whose structure is based on the linear combination, and it is usually used to predict some quantitative responses to some inputs(predictors).