Share this page!

Santiago Profile picture
Twitter logo
Feb 18 • 4 tweets • 1 min read
Applying dimensionality reduction Image
What’s your answer?
Based on the answers so far, this one seems to be easy.
The exciting part about writing multi-choice questions:

When I'm writing them, I have no idea how difficult they actually are.

Most of the time, my intuition is entirely wrong: Questions I think are easy turn out to be hard, while supposedly hard questions are easy.

• • •

Missing some Tweet in this thread? You can try to force a refresh
　

Keep Current with Santiago

Santiago Profile picture

Stay in touch and get notified when new unrolls are available from this author!

Read all threads

This Thread may be Removed Anytime!

PDF

Twitter may remove this content at anytime! Save it as PDF for later use!

Try unrolling a thread yourself!

how to unroll video
  1. Follow @ThreadReaderApp to mention us!

  2. From a Twitter thread mention us with a keyword "unroll"
@threadreaderapp unroll

Practice here first or read more on our help page!

More from @svpino

Santiago Profile picture
Feb 21
Every recommendation to start with machine learning focuses on a few building blocks:

• Linear Algebra
• Calculus
• Statistics and probabilities
• Fundamentals of machine learning

But there's also a different way.

Let's talk about it: ↓
Many companies are dying to start applying machine learning to their businesses.

Believe me: I talk to many of them every week.

Their main problem: they don't know where to start or how to get it done.
If you are reading this, you are probably part of one of these companies.

Heck, for all I know, most employees out there work for a company that's in this situation!

The demand for machine learning professionals is enormous!
Read 25 tweets
Santiago Profile picture
Feb 19
Learning about containers will open many doors for you.

If you are a Machine Learning Engineer, containerization is a must.

For the most part, "deploying machine learning" has a lot to do with containers.
There are a couple of ways you can approach this:

Understanding how containers work, building blocks, the standardized API, etc.

Or, you can start with Docker, find a few examples of how to use it, and progress from there.
I've been deploying things inside Docker containers for years now.

I'm sure I can't explain most of the things happening behind the scenes.

Anyone with that knowledge is in a much better position, for sure, but that doesn't mean that I can't get my work done correctly.
Read 4 tweets
Santiago Profile picture
Feb 17
This is how I split machine learning projects:

1. Project scoping
2. Data definition and preparation
3. Model training and error analysis
4. Deployment, monitoring, and maintenance

Here are 33 questions that most people forget to ask.
"Project scoping":

• What problem are we trying to solve?
• Why does it need to be solved?
• Do we truly need machine learning for this?
• What constraints do we have?
• What are the risks?
• What's the best approach to solving this?
• How do we measure progress?
Still under "Project scoping":

• What does success look like?
• How is our solution going to impact people?
• What could go wrong with our solution?
• What's the simplest version we could build?
Read 8 tweets
Santiago Profile picture
Feb 8
If you look at this code and think the answer is False, you aren't alone.

Nevertheless, we are all wrong: it returns True.

Read on to see what's happening.

↓
The clue here is the two consecutive operators next to each other.

Operator 1: ==
Operator 2: in

And we have "False" sandwiched in the middle.

The logical reaction is to parse the statement piece by piece. That's what I did.

But that's not how it works.
When it comes to answering this question, there are two camps:

1. Those who claim that "==" takes precedence.

2. Those who claim that "in" takes precedence.

Let's see what we get on both of these cases.
Read 11 tweets
Santiago Profile picture
Feb 6
Guess the output and don't cheat.

(This is Python) Image
If you pay careful attention you will realize the equal sign seems strangely elongated.

The reason is because it’s not an equal sign but an ==.

A font ligature causes this effect.

So, no assignment. It’s an equality.
If you are surprised about the answer here, look into “chained comparison” in Python so you can see how it works.
Read 4 tweets
Santiago Profile picture
Jan 29
When I started with machine learning, I always made the same mistake:

I confused a couple of metrics that look very similar but are entirely different.

Let's fix that for you.

↓
2. When we train a machine learning model, we need to compute how different our predictions are from the expected results.

For example, if we predict a house's price as $150,000, but the correct answer is $200,000, our "error" is $50,000.
3. There are multiple ways we can compute this error, but two common choices are:

• RMSE — Root Mean Squared Error
• MAE — Mean Absolute Error

These have different properties that will shine depending on the problem you want to solve.
Read 15 tweets

Did Thread Reader help you today?

Support us! We are indie developers!

This site is made by just two indie developers on a laptop doing marketing, support and development! Read more about the story.

Become a Premium Member ($3/month or $30/year) and get exclusive features!

Become Premium

Don't want to be a Premium member but still want to support us?

Make a small donation by buying us coffee ($5) or help with server cost ($10)

Donate via Paypal

Or Donate anonymously using crypto!

Ethereum

0xfe58350B80634f60Fa6Dc149a72b4DFbc17D341E copy

Bitcoin

3ATGMxNzCUFzxpMCHL5sWSt4DVtS8UqXpi copy

Thank you for your support!

Like this author's thread?

Get emails when @svpino has new unrolls!

Check out other threads from @svpino!

Read all threads by @svpino

:(