Key ML Terminology
 Feature: features are the input variables we feed into a network, it can be as simple as a signle number or more complex as an image (which in reality is a vector of numbers, where each pixel is a feature)

Label: is the thing we are predicting, it is normally refered as
y

Prediction: or predicted value if the value we predict with a previously trained model for a given output and it is refered as
y'
Regression vs. classification:
 A regression model predicts continuous values.
 A classification model predicts discrete values.
Linear Regression
Is a method for finding the straight line or hyperplane that best fits a set of points.
Line formula:
y = wx + b
Where:
w = Weights
x = Input features
b = Bias
Some convenient loss functions for linear regression are:
L_{2} Loss = (y – y’)^{2}
Mean Square Error: is the average squared loss per example over the whole dataset. To calculate MSE, sum up all the squared losses for individual examples and then divide by the number of examples
When training a model we want to minimize the loss as much as possible to make the model more accurate.