Santiago Profile picture
10 May, 15 tweets, 3 min read
A 6-step process that completely changed my life:

• Maximize what you don't learn
• Avoid schedules
• Use uncomfortable situations
• Learn as a byproduct
• Teach somebody else
• Circle back in a month

On how to learn efficiently and get ahead in life: ↓
Everything starts with maximizing the things I don't learn.

If I spend time on things that don't bring me value, I can't focus on what really matters to me.

By default, everything around me is noise until it's impossible to ignore.
If I don't see the value right away, I'll ignore it. Important things will make their way back to me.

Ignoring the noise makes space for what truly matters.
I make sure to learn new things every week.

This is a must.

But I don't set a specific time for this. I can't bound when and how learning happens. If I did, learning would be a chore.

Instead, I put myself in situations where I can't avoid learning.
Here are 3 situations I use to trick myself into learning new things:

• I volunteer at work to build things I don't know how to do.

• I play devil's advocate with the work of a smarter co-worker.

• I start writing an article on a new topic I don't know much about.
Situations that make me uncomfortable and stretch me are those that spark learning. I maximize those situations, and new knowledge always follows.

There's another benefit:

Learning becomes indivisible from using that knowledge.

Think about this.
"I'll learn a new thing, then find a way to apply that knowledge." This is backward.

Instead, I start making new things, and learning becomes the means to that end.

In other words: Focus on creating. Let everything else follow.
There's usually a lot I leave on the table when learning this way.

I'm a pragmatic person. I tend to cherry-pick what I need to get something working, then move on to the next challenge.

This gives me a shallow understanding of a bunch of things.

I don't like this.
Teaching people what I learn is my way to solve this.

You can't explain something and have other people understand unless you truly know the subject in and out.

I use my writing as a vehicle to teach others.
This is what I do as I'm learning new things:

I keep a list of notes with things I don't fully understand and jargon words that I can't explain.

When I'm ready to write, each note is a potential rabbit hole I'll explore.

I go as deep as I need to organize my ideas.
Quick summary so far:

• Minimize the noise
• Make myself uncomfortable
• Creator-first mentality
• Supplement learning with teaching

This gives me the best chance to incorporate new knowledge and do it frequently.

There's only one thing left.
I force myself to go back after some time and revisit what I learned.

Four weeks is usually a good timeline.

This is what I'm looking for:

• Do I still remember the thing?
• Has my understanding evolved?
Four weeks is enough time to surface the weak areas of what I learned.

This is what usually happens:

• The things I learned and used are still there.
• The things I learned but didn't use are gone.

This is time for reinforcement.
Whenever you hear me beating the same drum for quite a while, I'm probably trying to reinforce my understanding of it.

I write and explain the heck out of it until it becomes engrained in my brain.

This works.
Final thought:

Learning has to be fun, or it will be for nothing.

I suck at learning things because I have to. My brain actively rejects things that don't bring me joy.

If you want to learn while having fun, follow me @svpino, and I promise you won't regret it.

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More from @svpino

8 May
My recommendation to learn machine learning:

• Introduction to Python Programming (Udacity)
• Machine Learning Crash Course (Google)
• Machine Learning (Coursera)

In that order. They are all free. They are all amazing.

And take your time. This is a marathon, not a sprint.
Kaggle is an amazing place to practice what you learn.
And of course, there’s always my newsletter, and my Twitter account… if you truly want to learn machine learning, you definitely want to stay tuned!
Read 4 tweets
8 May
A topic that comes up in every interview.

Bias, variance, and their relationship with machine learning algorithms. One of the most basic concepts that you have to know by heart.

Here is a simple summary that you will easily remember.

Every machine learning algorithm deals with 3 types of errors:

1. Bias error
2. Variance error
3. Irreducible error

There's nothing we can do about #3.

Let's focus on the other two.

1/5
"Bias" refers to the assumptions the model makes to simplify the process of finding answers.

The more assumptions it makes, the more biased the model is.
Read 9 tweets
7 May
Do you know what scares me? Data labeling in machine learning.

We don't talk enough about it, and yet we can't do anything unless we solve this first. Labeling enough data is expensive or even outright impossible.

Some ideas to solve this problem.

Let's start with an example:

You have terrain and weather information for different locations. Your goal is to build a model that predicts where to drill to find oil.

How do you label this data? You drill to find out where the oil is.

This is ridiculously expensive.
To get around this problem, you need to minimize the number of labeled examples you need to build a good model.

1. Take the data
2. Select as few examples as possible
3. Drill those holes to come up with the labels
4. Train the model

How can you achieve #2?
Read 11 tweets
6 May
12 machine learning YouTube videos.

On libraries, algorithms, tools, and theory.

1. Jupyter Notebooks:

2. Pandas:

3. Matplotlib:

4. Seaborn:
5. Numpy:

6. Decision Trees:

7. Neural Networks:

8. Scikit-Learn:
Read 4 tweets
5 May
Machine learning education is broken.

If you are preparing for a research position, you are good. If you are looking to get out there and start solving problems, not even close.

Here are some thoughts so you can get ahead.

Most classes, courses, and books cover the same road.

They start with a dataset. They finish with a working model. The focus is always on everything that happens in between.

Dataset → Model.

This is great, but not enough.
Real-life situations rarely start with a dataset, and they never end after you finish building your model.

Applying machine learning successfully is hard.

Here are a few examples that you should keep in mind.
Read 13 tweets
1 May
A little over 12 years ago, the police started building a case against me.

That was stressful. They were watching. They wanted to take me off the streets.

Here is the story of how I fled Cuba and came to the United States.

After finishing college, I started taking freelance projects.

That was illegal. The Cuban government didn't allow people to make money working for foreign companies.

If you were lucky, you could get 2 years in jail. They called it "illicit enrichment."
We were a small group of friends. We met at my house every morning.

We paid a foreign national for Internet access. Cubans weren't allowed to buy it, so we had to get creative.

It was a 56kbs connection shared across 4 computers.
Read 13 tweets

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