Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Fixed some silly bugs in erm-p1 #8

Merged
merged 1 commit into from
Jul 14, 2024
Merged

Fixed some silly bugs in erm-p1 #8

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
3 changes: 1 addition & 2 deletions app/posts/erm-p1/page.mdx
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -12,7 +12,6 @@ import training_image from "./training.svg";

**Empirical Risk Minimization (ERM)** is a mathematical framework for performing supervised machine learning (ML). This will be **part 1** in a series of blog posts exploring ERM from the ground up.


In **part 1**, we will broadly cover the task of supervised machine learning in a non-rigorous manner. By the end of this post, you should understand what supervised machine learning entails, so that later parts can formalize these concepts using math.

<hr className="mt-3"/>
Expand Down Expand Up @@ -43,7 +42,7 @@ An ML researcher may choose to implement a _classification_ model for fraud dete
- _fraudulent_ - if the model believes the purchase is fraudulent
- _not fraudulent_ - otherwise

We'll defer a conversation on _how_ the model predict if a credit card purchase is fraudulent to the [Features and Labels](#features-and-labels) section.
We'll defer a conversation on _how_ the model predict if a credit card purchase is fraudulent to the [Training and Learning](#training-and-learning) section.
</Example>

## Features and Labels
Expand Down
Loading