Magson - Machine Learning Experiments

This is the official repositories with all of Magson's machine learning experiments and notes.

Experiments

Table of Contents

Tensorflow in Python

• Linear regression
• Polynomial regression

TensorflowJS

• Regression

Notes

ML Terminology

Machine learning systems learn how to combine input to produce useful predictions on never-before-seen data.

• Labels: is what we try to predict. E.g. the y variable in linear regression.
• Features: is an input variable. E.g. the x variable in linear regression.
• Model: defines the relationship between features and labels.

Regression VS. Classification

Regression model predicts continous values. E.g:

• What is the value of the house?
• What is the probability that a user clicks an ad?

Classification model predicts discrete values:

• Is mail spam or not spam?
• Is this image of a dog or cat?

Linear regression

y = mx + b

• y is the value we try to predict
• m is the slope of the line
• x is the input feature
• b is the y-intercept

By convention in machine learning, you'll write the equation for a model slightly differently:

y = b + wx

• y = predicted labels
• b = biases
• w = weights
• x = features