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Santiago Profile picture
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Feb 22 • 7 tweets • 3 min read
Do you want to take your machine learning skills to a new level?

Read this very carefully:

This is an opportunity for anyone who can't wait to apply machine learning to real-world challenges.

The best part: It's 100% free!

Read on for the details: ↓

pischool.link/AI10
The School of Artificial Intelligence @picampusschool starts its hands-on mentoring program on March 14.

8 weeks where you'll be working on real industry challenges!

You'll learn by doing as part of a team, and you'll have a mentor!

Honestly, it doesn't get better than this.
I want you to apply right now (you have absolutely nothing to lose!)

If you get approved, you'll get a full ride, 100% free, and 8 weeks later, your life will not be the same.

Go to this link, and send your application right away!

pischool.link/AI10
The program is mostly remote.

• Weeks 1 - 7: Remote
• Week 8: Live in Rome.

The program is full-time.

Imagine how much you'll get from fully dedicating yourself to this!

Here is the part that excites me the most about this opportunity:
If you can't travel to Rome for the last week of the program, apply anyways.

I'm pretty sure that the final week can be done remotely as well.

• • •

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More from @svpino

Santiago Profile picture
Feb 23
One of the most popular activation functions used in deep learning models is ReLU.

I asked: "Is ReLU continuous and differentiable?"

Surprisingly, a lot of people were confused about this.

Let's break this down step by step: ↓
Let's start by defining ReLU:

f(x) = max(0, x)

In English: if x <= 0, the function will return 0. Otherwise, the function will return x.
If you draw this function, you'll get the attached chart.

Notice there are no discontinuities in the function.

This should be enough to answer half of the original question: the ReLU function is continuous.

Let's now think about the differentiable part. Image
Read 14 tweets
Santiago Profile picture
Feb 21
The anatomy of ReLU.

Check your answer in the next tweet.
Answer here
I'm surprised this is shaping up the way it is.

Still, plenty of time left, but I would have expected the correct answer to pull ahead by now.
Read 7 tweets
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 27 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 18
Applying dimensionality reduction Image
What’s your answer?
Based on the answers so far, this one seems to be easy.
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

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