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Santiago Profile picture
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2 Mar, 6 tweets, 2 min read
Some hard skills that I use every day as a Machine Learning Engineer:

▫️ A whole lot of Python
▫️ TensorFlow, Keras, Scikit-learn
▫️ AWS SageMaker
▫️ Jupyter
▫️ SQL
▫️ Probabilities, Statistics
▫️ Google Spreadsheets (seriously!)
▫️ Software Engineering
General notions of linear algebra are useful, especially when you want to understand how certain things happen behind the scenes.

That being said, I don't consider myself an expert and it's not part of the day-to-day.

You could also use Excel.

I use Google Spreadsheets because it's in the cloud, and it's convenient for me. I don't have Microsoft Office installed, and as long as spreadsheets aren't crazy large, Google has what I need.

Anytime you need to get data from a database, you'll be dealing with some sort of query language.

SQL happens to be the standard one. Most relational database systems rely on it, and a lot of NoSQL systems use similar languages.

It's very similar to other Cloud providers, so from that standpoint, it is not expensive.

For personal use is probably overkill. If all you need is a notebook, use Google Colab instead.

I feel that if you truly need SageMaker, you can afford it.

In machine learning, we need a mechanism to quantify uncertainty, and probabilities give us that.

Things from Bayes to Maximum Likelihood Estimation come up from time to time.

But it's not about specific techniques but general understanding.

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Santiago Profile picture
3 Mar
The two questions related to neural networks that I hear most often:

▫️ How many layers should I use?
▫️ How many neurons per layer should I use?

There are some rules of thumb that I'll share with you after you get your ☕️ ready.

🧵👇
First, let's get this out of the way:

A neural network with a single hidden layer can model any function regardless of how complex it is (assuming it has enough neurons.)

Check the "Universal Approximation Theorem" if you don't believe me.

↓
So, if we can do it all with a single layer, why bother adding more layers?

Well, it turns out that a neural network with a single layer will overfit really quick.

The more neurons you add to it, the better it will become at memorizing stuff.

That is bad news.

↓
Read 10 tweets
Santiago Profile picture
2 Mar
Let's talk about how you can build your first machine learning solution.

(And let's make sure we piss off half the industry in the process.)

Grab that ☕️, and let's go! 🧵
Contrary to popular belief, your first attempt at deploying machine learning should not use TensorFlow, PyTorch, Scikit-Learn, or any other fancy machine learning framework or library.

Your first solution should be a bunch of if-then-else conditions.
Regular, ol' conditions make for a great MVP solution to a machine learning wannabe system.

Pair those conditions with a human, and you have your first system in production!

Conditions handle what they can. Humans handle the rest.
Read 16 tweets
Santiago Profile picture
2 Mar
If you want to start with Machine Learning and need some guidance, I want to give you access to my entire course for $10. Today only.

And if you don't like it, you pay $0. But I promise you'll love it!

Thanks to the 100+ of you who already bought it!

👉 gumroad.com/l/kBjbC/50000
If you can’t afford this, reply below explaining how do you think this will help you. I’ll give away 10 copies for free.
Thanks to everyone that has taken advantage of this offer so far!

There are still a few more hours left.

If starting with machine learning feels overwhelming, then this is for you.

gumroad.com/l/kBjbC/50000
Read 4 tweets
Santiago Profile picture
1 Mar
Let's talk about learning problems in machine learning:

▫️ Supervised Learning
▫️ Unsupervised Learning
▫️ Reinforcement Learning

And some hybrid approaches:

▫️ Semi-Supervised Learning
▫️ Self-Supervised Learning
▫️ Multi-Instance Learning

Grab your ☕️, and let's do this👇
Supervised Learning is probably the most common class of problems that we have all heard about.

We start with a dataset of examples and their corresponding labels (or answers.)

Then we teach a model the mapping between those examples and the corresponding label.

[2 / 19]
The goal of these problems is for a model to generalize from the examples that it sees to later answer similar questions.

There are two main types of Supervised Learning:

▫️ Classification → We predict a class label
▫️ Regression → We predict a numerical label

[3 / 19]
Read 19 tweets
Santiago Profile picture
28 Feb
Here are the best 10 machine learning threads I posted in February.

They go all the way from beginner-friendly content to a broader dive into specific machine learning concepts and techniques.

I'd love to hear which one is your favorite!

🧵👇
Having to pick only 10 threads is painful. I always struggle to decide what should stay out of the list.

This, however, is a great incentive when I'm writing the content: I have to compete against myself to make sure what I write ends up being part of the list!

[2 / 13]
[Thread 1]

An explanation about three of the most important metrics we use: accuracy, precision, and recall.

More specifically, this thread shows what happens when we focus on the wrong metric using an imbalanced classification problem.

[3 / 13]

Read 13 tweets
Santiago Profile picture
27 Feb
For the first time yesterday, I set up a project using a Development Container in Visual Studio Code and it immediately hit me:

✨ This is the way going forward! 🤯

If you haven't used this yet, here are some thoughts.

👇
The basic idea: you can run your entire development environment inside a container.

Every time you open your project, @code prepares and runs your container.

[2 / 7]
There are several advantages to this:

First of all, your entire team will run exactly the same environment, regardless of their preferred operating system, folder structure, existing libraries, etc.

Everyone will have a mirrored experience.

[3 / 7]
Read 8 tweets

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