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Santiago 🎃

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14 Oct, 9 tweets, 3 min read

Machine Learning 101:

▫️ Overfitting sucks ▫️

Here is what you need to know.

🧵👇

Overfitting is probably the most common problem when training a Machine Learning model (followed very close by underfitting.)

Overfitting means that your model didn't learn much, and instead, it's just memorizing stuff.

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Overfitting may be misleading: during training, it looks like your model learned awesomely well.

Look at the attached picture. It shows how the accuracy of a sample model increases as it's being trained.

The accuracy reaches close to 100%! That's awesome!

Or, is it?

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The red line is the accuracy of the model predicting the same data we used to train it.

That sounds suspicious.

Imagine you studying a bunch of questions over and over again. Yeah, maybe you can answer all of those, but are you really understanding the concepts?

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We need to evaluate what we are doing by using the model with new data. Questions that the model hasn't seen before.

Look at the attached image. The new accuracy is the yellow line.

Notice what happens around epoch 50: the accuracy starts decreasing! 😱

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You have the training accuracy continuously increasing in the previous figure, but the validation accuracy stopping at around 50%.

That's overfitting!

After 50 epochs, your model is not learning anything useful. All it's doing is memorizing the training data set.

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Why is it called "overfitting"?

Because the model "fits" the target function "too well" to the data.

Our target function is in red color.

Model A is 100% accurate on the training data. But this function is clearly remembering each data point!

Model B generalizes well.

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An approach to understanding whether overfitting impacts you is to train the model with some of the data only.

Then, after your model finishes training, you can use the rest of the data to ensure the model is generalizing well.

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Here are two resources that will help you go from here:

▫️Overfitting on Wikipedia: en.wikipedia.org/wiki/Overfitti…

▫️Overfitting on Youtube:

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More from @svpino

Santiago 🎃

@svpino

13 Oct

Transfer Learning.

It sounds fancy because it is.

This is a thread about one of the most powerful tools that make possible that knuckleheads like me achieve state-of-the-art Deep Learning results on our laptops.

🧵👇

Deep Learning is all about "Deep" Neural Networks.

"Deep" means a lot of complexity. You can translate this to "We Need Very Complex Neural Networks." See the attached example.

The more complex a network is, the slower it is to train, and the more data we need to train it.

👇

To get state-of-the-art results when classifying images, we can use a network like ResNet50, for example.

It takes around 14 days to train this network with the "imagenet" dataset (1,300,000+ images.)

14 days!

That's assuming that you have a decent (very expensive) GPU.

👇

Read 10 tweets

Santiago 🎃

@svpino

12 Oct

When I heard about Duck Typing for the first time, I had to laugh.

But Python 🐍 has surprised me before, and this time was no exception.

This is another short thread 🧵 that will change the way you write code.

👇

Here is the idea behind Duck Typing:

▫️If it looks like a duck, swims like a duck, and quacks like a duck, then it probably is a duck.

Taking this to Python's world, the functionality of an object is more important than its type. If the object quacks, then it's a duck.

👇

Duck Typing is possible in dynamic languages (Hello, JavaScript fans 👋!)

Look at the attached example. Notice how "Playground" doesn't care about the specific type of the supplied item. Instead, it assumes that the item supports the bounce() method.

👇

Read 8 tweets

Santiago 🎃

@svpino

11 Oct

This is ridiculous.

I've been coding in Python 🐍 since 2014. Somehow I've always resisted embracing one of their most important principles.

This is a short thread 🧵about idiomatic Python.

👇

Python is all about readability.

One of its core principles is around writing explicit code.

Explicitness is about making your intentions clear.

Take a look at the attached code. It's one of those classic examples showing bad versus good.

See the difference?

👇

There are more subtle ways in which Python encourages explicitness.

This example shows a function that checks whether two keys exist in a dictionary and adds them up if they do.

If one of the keys doesn't exist, the function returns None.

Nothing wrong here, right?

👇

Read 9 tweets

Santiago 🎃

@svpino

10 Oct

I had a breakthrough that turned a Deep Learning problem on its head!

Here is the story.
Here is the lesson I learned.

🧵👇

No, I did not cure cancer.

This story is about a classification problem —specifically, computer vision.

I get images, and I need to determine the objects represented by them.

I have a ton of training data. I'm doing Deep Learning.

Life is good so far.

👇

I'm using transfer learning.

In this context, transfer learning consists of taking a model that was trained to identify other types of objects and leverage everything that it learned to make my problem easier.

This way I don't have to teach a model from scratch!

👇

Read 11 tweets

Santiago 🎃

@svpino

8 Oct

Here is a formula for growth.

(Not the only one, but one that works.)

People follow you when they are afraid of missing out on what you have to say.

People get afraid of missing out when they meet you and have a chance to realize that they want more of you.

🧵👇

Then, we can conclude that there are two steps:

1. You need to get in front of people by posting "shareable" content.

2. Once people meet you, you need to hook them so they follow you instead of moving on.

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In Twitter, shareable content means content that people want to engage with:

▫️They like it
▫️They leave a comment
▫️They retweet it

There are multiple ways to create shareable content that spreads quickly.

👇

Read 10 tweets

Santiago 🎃

@svpino

8 Oct

A real-life Machine Learning solution that works.

Here is a breakdown of every piece and how they work together.

🧵👇

There's a website.

Users upload a group of pictures of an item and select the category it belongs to.

The system returns how much money the item is worth.

👇

Before:

▫️A group of people reviewed the pictures submitted by the user and decided how much the item was worth.

Today:

▫️The system quotes some of the items automatically (some still have to go through humans for a quote.)

👇

Read 11 tweets

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