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16 Mar, 5 tweets, 2 min read
5 Python 🐍 package managers that I'm not using anymore:

▫️ conda
▫️ virtualenv
▫️ venv
▫️ pipenv
▫️ poetry

🤷‍♂️

Instead, for several weeks now, I've been using development containers in Visual Studio Code.

Life-changing. Give 'em a try.
Here is a thread I wrote a few weeks back when I started using them:
An important note: here I’m referring to the “virtual environment” capabilities of these tools. I still need to pip modules down.

But I’ve been isolating environments with the containers instead.
The code is not dependent on the IDE. The environment is.
I don't create a virtual environment. Instead, I put the entire development environment inside a Docker container.

This includes both Python modules plus everything else I need to develop, like @code extensions, npm libraries, system libraries, etc.

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More from @svpino

Santiago Profile picture
17 Mar
Here are some of the features that make Python 🐍 a freaking cool language.

🧵👇
1. You can slice and dice arrays very easily. Image
2. What's even better: negative indexing is really cool, and you can use it to refer to items from the last element in the list. Image
Read 15 tweets
Santiago Profile picture
15 Mar
You aren't doing yourself any favors if you aren't throwing away your validation data regularly.

It's painful, I know, but you are looking for trouble if you don't do it.

Let's talk about what happens with your data and your model.

Grab the ☕️, and let's do this thing. 🧵👇
Every machine learning tutorial teaches you about splitting your dataset.

They either go with train/test or train/validation/test. Nomenclature doesn't matter here. You just need to understand how each one of these is used.

Here is a thread about this:

Let's think of a neural network and focus on the train set for a second.

We use this to train our model. The data on this set is the one the network uses to adjust the weights.

And, of course, the model will get really good at solving this set.
Read 8 tweets
Santiago Profile picture
15 Mar
My recommendation to learn machine learning:

▫️ Machine Learning Crash Course (Google)
▫️ Machine Learning (Coursera)

Take them in order. They are both free. They are both amazing.

(Before you embark on this journey, make sure you feel comfortable writing Python 🐍.)
Not really. Nothing has changed with the fundamentals. The course is as relevant today as it was back in 2010.
Experience will help you make a few sensible choices that you can later test.
Read 8 tweets
Santiago Profile picture
14 Mar
A good way to understand how shit works is by breaking it down as much as you can.

Here is some code showing Dropout working on an array. And this is a thread explaining how it works.

☕️🧵👇
First, the code.

I want you to notice that Dropout does a couple of things:

▫️ It zeroes-out a percentage of the units.
▫️ It scales the remaining units to account for the missing values.

The second one wasn't obvious to me.
Remember those kids from school that sat together and copied from each other during exams?

They aced every test but were hardly brilliant, remember?

Eventually, the teacher had to set them apart. That was the only way to force them to learn.
Read 9 tweets
Santiago Profile picture
12 Mar
Do you like Star Wars?

Let's talk about John, a machine learning engineer.

He is building a model to predict whether people will like a future Star Wars episode.

This is the story of how John screwed things up.

☕️🧵👇
They are screening the new Star War episode, and the theater seems like a good place to collect some information to create the model.

John goes right in and gives an optional survey to the people sitting in the middle of the theater right before the movie starts.
John does the same for a couple of weeks, then goes back to his computer and puts together a model.

It turns out that the model sucks.

The new episode doesn't do well despite the model predicting the opposite.

What happened?
Read 8 tweets
Santiago Profile picture
12 Mar
“Autoencoders and rotten bananas” is the first story trying to teach you something new about machine learning.

A lot of work to get this out, and I hope you enjoy it as much as I did writing it.

digest.underfitted.io/archive/364757
This is such a great question!

Why in the world do we need autoencoders when we have pretty good compression algorithms?

Autoencoders will never do better than our existing techniques (like for example, JPEG encoding).

JPEG works for any image. Autoencoders only work for the type of images that it was trained on.

That's a big disadvantage.

But only if you are trying to use them to compress images.

Here is a killer application of autoencoders: anomaly detection.
Read 5 tweets

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