Machine learning fundamentals (I): Cost functions and gradient descent
**This is part one of a series on machine learning fundamentals.
Machine learning fundamentals (II): Neural networks
A quick and **tidy** data analysis
Machine Learning week 1: Cost Function, Gradient Descent and Univariate Linear Regression
I have started doing Andrew Ng’s popular machine learning course on Coursera. The first week covers a lot, at least for someone who hasn’t touched much calculus for a few years
- Cost Functions (mean difference squared)
- Gradient Descent
- Linear Regression
Understanding and Calculating the Cost Function for Linear Regression
his post will focus on the properties and application of cost functions, how to solve it them by hand. Then we will implement the calculations twice in Python, once with
for loops, and once with
vectors using numpy. This goes into more detail than my previous article about linear regression, which was more a high level summary of the concepts.