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You can explain the Bayes formula in pure English.

Even without using any mathematical terminology.

Despite being overloaded with seemingly complex concepts, it conveys an important lesson about how observations change our beliefs about the world.

Let's take it apart!

Even without using any mathematical terminology.

Despite being overloaded with seemingly complex concepts, it conveys an important lesson about how observations change our beliefs about the world.

Let's take it apart!

Essentially, the Bayes formula describes how to update our models, given new information.

To understand why, we will look at a simple example with a twist: coin tossing with an unfair coin.

To understand why, we will look at a simple example with a twist: coin tossing with an unfair coin.

Let's suppose that we have a magical coin! It can come up with heads or tails when tossed, but not necessarily with equal probability.

The catch is, we don't know the exact probability. So, we have to perform some experiments and statistical estimation to find that out.

The catch is, we don't know the exact probability. So, we have to perform some experiments and statistical estimation to find that out.

It is the weekend now, so let's talk about something different, but still awesome and beautiful!

This image has been my desktop wallpaper for years.

Can you guess what is it?

This machine represents one of the most brilliant ideas I have seen. (Answer in the next tweet.)

This image has been my desktop wallpaper for years.

Can you guess what is it?

This machine represents one of the most brilliant ideas I have seen. (Answer in the next tweet.)

This is the Wankel engine, a surprisingly innovative type of internal combustion engines.

Why is it so brilliant? In short, because it parallelizes the classical four-stage Otto cycle, all in one chamber!

To elaborate a bit, let's see how a four-stroke piston engine works!

Why is it so brilliant? In short, because it parallelizes the classical four-stage Otto cycle, all in one chamber!

To elaborate a bit, let's see how a four-stroke piston engine works!

The common four-stroke piston engine essentially has four stages:

1. Intake

2. Compression

3. Combustion

4. Exhaust

These happen in sequence inside a cylinder-shaped chamber, as shown below.

(Gifs and images in the thread are all from Wikipedia.)

1. Intake

2. Compression

3. Combustion

4. Exhaust

These happen in sequence inside a cylinder-shaped chamber, as shown below.

(Gifs and images in the thread are all from Wikipedia.)

At telesto.ai, we realized that we made a crucial mistake in organizing our workflow.

Up until now, we always started with the backend API when developing new features. Then, we added the UI.

You definitely shouldn't do that.

Let me explain why!

Up until now, we always started with the backend API when developing new features. Then, we added the UI.

You definitely shouldn't do that.

Let me explain why!

You always notice crucial flaws in the UI when seeing it for the first time.

It may be hard to use or straight-up lack functionality that you missed during planning.

However, changes require backend modifications as well. You have to do the work twice!

It may be hard to use or straight-up lack functionality that you missed during planning.

However, changes require backend modifications as well. You have to do the work twice!

So, our workflow is now the following.

1. Sketch the UI in Figma.

2. Walk through the user flow several times.

3. Spot flaws and correct the UI.

4. Repeat 1-3 at least once.

5. Move on to design and implement corresponding backend functionality.

1. Sketch the UI in Figma.

2. Walk through the user flow several times.

3. Spot flaws and correct the UI.

4. Repeat 1-3 at least once.

5. Move on to design and implement corresponding backend functionality.

Mean Square Error is one of the most ubiquitous error functions in machine learning.

Did you know that it arises naturally from Bayesian estimation? That seemingly rigid formula has a deep probabilistic meaning.

💡 Let's unravel it! 💡

Did you know that it arises naturally from Bayesian estimation? That seemingly rigid formula has a deep probabilistic meaning.

💡 Let's unravel it! 💡

If you are not familiar with the MSE, first check out this awesome explanation by @haltakov!

In the following, we are going to dig deep into the Bayesian roots of the formula!

( )

In the following, we are going to dig deep into the Bayesian roots of the formula!

( )

Why is matrix multiplication defined the way it is?

When I first learned about it, the formula seemed too complicated and totally unintuitive! I wondered, why not just multiply elements at the same position together?

💡 Let me explain why! 💡

When I first learned about it, the formula seemed too complicated and totally unintuitive! I wondered, why not just multiply elements at the same position together?

💡 Let me explain why! 💡

Expected value is one of the most fundamental concepts in probability theory and machine learning.

Have you ever wondered what it really means and where does it come from?

The formula doesn't tell the entire story right away.

💡 Let's unravel what is behind the scenes! 💡

Have you ever wondered what it really means and where does it come from?

The formula doesn't tell the entire story right away.

💡 Let's unravel what is behind the scenes! 💡

First, let's take a look at a simple example.

Suppose that we are playing a game. You toss a coin, and

• if it comes up heads, you win $1,

• but if it is tails, you lose $2.

Should you even play this game with me? 🤔

We are about to find out!

Suppose that we are playing a game. You toss a coin, and

• if it comes up heads, you win $1,

• but if it is tails, you lose $2.

Should you even play this game with me? 🤔

We are about to find out!