When machine learning met crypto art... they fell in love ❤️

The Decentralized Autonomous Artist (DAA) is a concept that is uniquely enabled by these technologies.

Meet my favorite DAA - Botto.

Let me tell you how it works 👇
Botto uses a popular technique to create images - VQGAN+CLIP

In simple terms, it uses a neural network model generating images (VQCAN) guided by the powerful CLIP model which can relate images to text.

This method can create stunning visuals from a simple text prompt!

👇
Creating amazing images, though, requires finding the right text prompt

Botto is programmed by its creator - artist Mario Klingemann (@quasimondo), but it creates all art itself. There is no human intervention in the creation of the images!

Botto is trained by the community 👇
@quasimondo The Botto Community

Botto asks its community for feedback and learns from it. Every week, Botto creates 350 new images and the members of the community vote for the pieces they like.

The feedback is then incorporated into the creations for the next weeks.

👇
@quasimondo At the end of every week, the best piece is sold at auction.

And Botto has been quite successful - it has generated $1.9M from 19 auctions. The most expensive piece, Scene Precede, sold for $430,000!

superrare.com/bottoproject

👇
@quasimondo Now, this is a lot of money, so how are they used?

The money goes back to the community!

The mechanism is quite interesting, but first, I need to tell you about the $BOTTO token.

👇
@quasimondo $BOTTO is a crypto coin that serves as a membership token for the community.

Only people holding $BOTTO are allowed to vote on the art. Every member gets voting points proportional to how much $BOTTO they have staked.

$BOTTO can be bought on decentralized exchanges.

👇
@quasimondo The $BOTTO supply is limited to max 100,000,000 tokens. In fact, the supply is decreasing - after every auction, Botto uses the proceeds to buy $BOTTO and burn it.

This makes the remaining supply more valuable and drives the price up. That's how the Botto community profits.

👇
@quasimondo Additionally, $BOTTO holders can earn more tokens by providing liquidity on UniSwap or by staking.

The tokenomics of the project is quite interesting because it creates incentives for people to stake their tokens and train Botto to become a better artist.

👇
@quasimondo And finally, Botto's presence is not limited to the Metaverse!

It travels the world accompanied by its creator @quasimondo to present its work at various events and in physical galleries.

👇
@quasimondo If you want to learn more about Botto, follow it @bottoproject and visit the website botto.com.

I also highly recommend reading this thread about how Botto was created:

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More from @haltakov

Mar 4
Machine Learning Formulas Explained! 👨‍🏫

This is the formula for the Binary Cross Entropy Loss. It is commonly used for binary classification problems.

It may look super confusing, but I promise you that it is actually quite simple!

Let's go step by step 👇

#RepostFriday Image
The Cross-Entropy Loss function is one of the most used losses for classification problems. It tells us how well a machine learning model classifies a dataset compared to the ground truth labels.

The Binary Cross-Entropy Loss is a special case when we have only 2 classes.

👇
The most important part to understand is this one - this is the core of the whole formula!

Here, Y denotes the ground-truth label, while Ŷ is the predicted probability of the classifier.

Let's look at a simple example before we talk about the logarithm... 👇 Image
Read 13 tweets
Feb 25
There are two problems with ROC curves

❌ They don't work for imbalanced datasets
❌ They don't work for object detection problems

So what do we do to evaluate our machine learning models properly in these cases?

We use a Precision-Recall curve.

Thread 👇

#RepostFriday
Last week I wrote another detailed thread on ROC curves. I recommend that you read it first if you don't know what they are.



Then go on 👇
❌ Problem 1 - Imbalanced Data

ROC curves measure the True Positive Rate (also known as Accuracy). So, if you have an imbalanced dataset, the ROC curve will not tell you if your classifier completely ignores the underrepresented class.

Let's take an example confusion matrix 👇
Read 20 tweets
Feb 24
Is your machine learning model performing well? What about in 6 months? 🤔

If you are wondering why I'm asking this, you need to learn about 𝗰𝗼𝗻𝗰𝗲𝗽𝘁 𝗱𝗿𝗶𝗳𝘁 and 𝗱𝗮𝘁𝗮 𝗱𝗿𝗶𝗳𝘁.

Let me explain this to you using two real world examples.

Thread 👇
Imagine you are developing a model for a self-driving car to detect other vehicles at night.

Well, this is not too difficult, since vehicles have two red tail lights and it is easy to get a lot of data. You model works great!

But then... 👇 Image
Car companies decide to experiment with red horizontal bars instead of two individual lights.

Now your model fails to detect these cars because it has never seen this kind of tail light.

Your model is suffering from 𝗰𝗼𝗻𝗰𝗲𝗽𝘁 𝗱𝗿𝗶𝗳𝘁

👇 ImageImage
Read 11 tweets
Feb 22
Math is not very important when you are using a machine learning method to solve your problem.

Everybody that disagrees, should study the 92-page appendix of the Self-normalizing networks (SNN) paper, before using
torch.nn.SELU.

And the core idea of SNN is actually simple 👇 ImageImageImageImage
SNNs use an activation function called Scaled Exponential Linear Unit (SELU) that is pretty simple to define.

It has the advantage that the activations converge to zero mean and unit variance, which allows training of deeper networks and employing strong regularization.

👇 ImageImage
There are implementations both in PyTorch (torch.nn.SELU) and TensorFlow (tf.keras.activations.selu).

You need to be careful to use the correct initialization function and dropout, but this is well documented.

The code is open-source as well: github.com/bioinf-jku/SNNs

👇
Read 9 tweets
Feb 21
This is like an NFT in the physical world

This is a special edition BMW 8 series painted by the famous artist Jeff Koons. A limited-edition of 99 with a price of $350K - about $200K more than the regular M850i.

If you think about it, you'll see many similarities with NFTs

👇 Image
Artificially scarce

BMW can surely produce (mint 😅) more than 99 cars with this paint. The collection size is limited artificially in order to make it more exclusive.

Same as most NFT collections - they create artificial scarcity.

👇
Its price comes from the story

The $200K premium for the "paint" is purely motivated by the story around this car - it is exclusive, it is created by a famous artist, it is a BMW Art Car.

It is not faster, more reliable, or more economic. You are paying for the story.

👇
Read 10 tweets
Feb 18
Did you ever want to learn how to read ROC curves? 📈🤔

This is something you will encounter a lot when analyzing the performance of machine learning models.

Let me help you understand them 👇

#RepostFriday
What does ROC mean?

ROC stands for Receiver Operating Characteristic but just forget about it. This is a military term from the 1940s and doesn't make much sense today.

Think about these curves as True Positive Rate vs. False Positive Rate plots.

Now, let's dive in 👇
The ROC curve visualizes the trade-offs that a binary classifier makes between True Positives and False Positives.

This may sound too abstract for you so let's look at an example. After that, I encourage you to come back and read the previous sentence again!

Now the example 👇
Read 19 tweets

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