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Santiago 🎃

@svpino

22 Oct

A quick, non-technical explanation of Dropout.

(As easy as I could make it.)

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Remember those two 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.

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The same happens with neural networks.

Sometimes, a few hidden units create associations that, over time, provide most of the predictive power, forcing the network to ignore the rest.

This is called co-adaptation, and it prevents networks from generalizing appropriately.

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Read 7 tweets

Santiago 🎃

@svpino

21 Oct

I always get Normalization and Standardization mixed up.

But they are different.

Notes about them and why do we care.

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Feature scaling is key for a lot of Machine Learning algorithms to work well.

We always want all of our data on the same scale.

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Imagine we are working with a dataset of workers.

"Age" will range between 16 and 90.
"Salary" will range between 15,000 and 150,000.

Huge disparity!

Salary will dominate any comparisons because of its magnitude.

We can fix that by scaling both features.
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Read 7 tweets

Santiago 🎃

@svpino

20 Oct

I'm a full-on AI proponent.

But I really don't like the idea of facial recognition software.

This is why.

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▫️It violates our right to privacy

Do you really want thousands of photos with your face stored in hundreds of databases all over the place?

Photos that will be automatically tagged with your personal information.

And you won't have any control over this.

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▫️Lack of regulations makes this scary.

Who will be able to use this? Do we have to give consent? Can we trust this? How is this information going to be used? With what purposes?

Are we gonna get tracked every time, everywhere?

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Read 9 tweets

Santiago 🎃

@svpino

19 Oct

Overfitting sucks.

Here are 7 ways you can deal with overfitting in Deep Learning neural networks.

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A quick reminder:

When your model makes good predictions on the same data that was used to train it but shows poor results with data that hasn't seen before, we say that the model is overfitting.

The model in the picture is overfitting.

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1⃣ Train your model on more data

The more data you feed the model, the more likely it will start generalizing (instead of memorizing the training set.)

Look at the relationship between dataset size and error.

(Unfortunately, sometimes there's no more data.)

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Read 10 tweets

Santiago 🎃

@svpino

18 Oct

Bias vs. variance in 13 charts.

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Here is a sample 2-dimensional dataset.

(We are just representing here the training data.)

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The red line represents a model.

Let's call it "Model A."

A very simple model. Just a straight line.

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Read 15 tweets

Santiago 🎃

@svpino

17 Oct

Wanna maximize the potential reward of every hour you spend?

Here is a tangible way to do this when building real-life Machine Learning solutions.

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Complex systems usually depend on multiple components working together to produce a solution.

Imagine a pipeline like this, where the input goes through 4 different components before getting to the appropriate output.

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After everything is said and done, let's imagine this system is correct 60% of the time.

That sucks. We need to improve it.

Unfortunately, we tend to prioritize work in those areas where we *think* there's value. Even worse, areas that are easy or fun to change.

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Read 9 tweets

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