Santiago Profile picture
12 Mar, 5 tweets, 2 min read
“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.
Imagine you train an autoencoder with a dataset of circuit boards that are correctly configured.

As soon as you give it an incorrect board, the autoencoder will have a hard time reproducing it, which will tell you there's a problem.

Just like the example of the rotten 🍌.
The idea is for the autoencoder to get really good at reproducing the original data. That's what "learning" means in this context.

An autoencoder is just a neural network, so yes, backpropagation happens here.

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More from @svpino

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
11 Mar
Before you start building a machine learning model, you need a baseline.

I find it helpful to think about 3 different levels and tackle them in order.

Here is how I do this: ☕️🧵👇
▫️ Level 1: The human baseline

Before anything else happens, I find it useful to understand how humans do when solving the problem.

This gives me the ceiling that I should aspire to (or maybe even beat it, if I'm lucky!)
Sometimes, the human baseline will hint at whether a model is a feasible solution for the problem.

For example, if the data doesn't contain information that we can use to make predictions, humans will do very poorly. This will save us a lot of work!
Read 10 tweets
11 Mar
"Do I need a Ph.D. or a Master's degree to work as a machine learning engineer?"

No.

A lot of companies ask for degrees to weed out people that apply to jobs prematurely.

If you have the required skills and show your experience, the degree will not matter.
Both are valid paths and it will come down to your personal preference or what you need to accomplish

If you aren’t sure, online courses give you less risk upfront.
There are specific positions that do require (and will continue to do so) a degree in a related field.

But the industry has changed in the last few years. Fewer places require degrees anymore, and the tendency will continue in that direction.

Read 5 tweets
10 Mar
Let's talk a little bit about machine learning in the real world.

A seemingly simple classification problem that turns ugly quickly.

Hopefully, this gives you an idea of what it takes to put some of the pieces together.

Grab your ☕️, and let's do this thing!

🧵👇
Let's imagine a system where you could sell your stuff.

You submit a bunch of pictures of an object, and the system recommends a price range in which the object could be sold.

Let's focus on classifying the object from the pictures.
An image classification problem sounds simple enough. There are 1,000 examples out there!

Unfortunately, getting value from these systems requires a lot of considerations.

Let me throw a lot of ideas to you. This 🧵 is messy, just like a potential solution to the problem.
Read 19 tweets
9 Mar
Here is an underrated machine learning technique that will give you important information about your data and model.

Let's talk about learning curves.

Grab your ☕️ and let's do this thing!

🧵👇
Start by creating a model. Something simple. You are still exploring what works and what doesn't, so don't get fancy yet.
We are now going to plot the loss (model error) vs. the training dataset size. This will help us answer the following questions:

▫️ Do we need more data?
▫️ Do we have a bias problem?
▫️ Do we have a variance problem?
▫️ What's the ideal picture?
Read 16 tweets
8 Mar
This week, I'll be on Twitter Spaces with amazing company!

We'll be talking about some cool machine learning techniques. Each one of us, a different one.

Save the date, and you can join us from your mobile phone right from the Twitter application.
We are planning to record this session, but... But, we will be recording the screen of an iPhone and some other weird stuff to try and get the audio out.

Not the best process, but we will try to get clean audio out of this.
If everything works, @haltakov will make the audio available (likely in the form of a podcast.) Where and how are details that we'll share when we know.

If the audio comes out too crappy, we will probably not bother because it won't be useful for you anyway.
Read 6 tweets

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