17/10/31 MWhite's learning notes
Supervised Learning & Unsupervised Learning
Regression & Classification
Supervised Learning
In supervised learning, we are given a data set and already know what our correct output should look like, having the idea that there is a relationship between the input and the output.
Supervised learning problems are categorized into "regression" and "classification" problems. In a regression problem, we are trying to predict results within a continuous output, meaning that we are trying to map input variables to some continuous function. In a classification problem, we are instead trying to predict results in a discrete output. In other words, we are trying to map input variables into discrete categories.
Unsupervised Learning
Unsupervised learning allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we don't necessarily know the effect of the variables.
We can derive this structure by clustering the data based on relationships among the variables in the data.
With unsupervised learning there is no feedback based on the prediction results.
Cost function
The relationship between hypothesis function and cost function
we should try to minimize the cost function.
Two dimension
Gradient descent
Gradient descent is a kid of algorithms.
And do remember update the parameters at the same time. (Simultaneously)
alpha is learning rate (not too small, not too large)
Gradient Descent For Linear Regression
The point of all this is that if we start with a guess for our hypothesis and then repeatedly apply these gradient descent equations, our hypothesis will become more and more accurate.
Matrices and Vectors
Matrices are not commutative: A?B≠B?A
Matrices are associative: (A?B)?C=A?(B?C)
identity matrix
inverse pinv(A)
transposition AT
