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15 Mar, 8 tweets, 3 min read
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.
Python is a prerequisite for the first course, yes. But generally speaking, I usually recommend people to learn Python before embarking in machine learning.

(Unless you are planning to approach it with a different language.)
Good question!

I find it hard to measure, so I usually say "as soon as you feel comfortable solving problems, you are ready" but I understand this is not actionable.

I'd recommend starting without worrying too much about the end.

And if you are looking for a complete step by step guide on how to get started: gum.co/kBjbC

The good part: it will cost you $0 if you think it doesn't work for you.
I actually think this is an advantage and not a problem.

Yes, I know everyone wants it to be on Python, but Octave's syntax forces you to forget about the coding part and simply focus on the theory.

Don't let this discourage you.

The Kaggle tutorials are definitely a great way to get started, for sure!

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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
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
Santiago Profile picture
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
Santiago Profile picture
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

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