First officially approved Level 3 self-driving system in Germany.

This is significant because it is the first time an autonomous system that takes the *driving responsibility* from the driver is approved for mass production!

europe.autonews.com/automakers/mer…

👇
The main difference between Level 2 and Level 3 systems is that self-driving systems become legally responsible for the actions of the cars when in autonomous mode!

All driver assist systems on the market now (including Tesla) are Level 2 systems.



👇
While Waymo and Cruise have Level 4 systems running as a beta in some cities, there are different challenges putting this tech in consumer vehicles and in cars that don't have a huge sensor rack costing tens of thousands of dollars on the roof.

👇
Sure, the use-cases of Mercedes' system are very limited - it only works in traffic jams on the Autobahn, but if you think about it, these are really the cases where you need self-driving most.

It will be interesting to see how this develops and how accidents are handled!

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More from @haltakov

17 Dec
How to evaluate your ML model? 📏

Your accuracy is 97%, so this is pretty good, right? Right? No! ❌

Just looking at the model accuracy is not enough. Let me tell you about some other metrics:
▪️ Recall
▪️ Precision
▪️ F1 score
▪️ Confusion matrix

Let's go 👇

#RepostFriday
We'll use this example in the whole thread - classifying traffic light colors (e.g. for a self-driving car).

Yellow traffic lights appear much less often, so our dataset may look like this.

This means our model could reach 97% accuracy, by ignoring all 🟡 lights. Not good!

👇 Image
Let's assume now that we trained our model and we get the following predictions.

Do you think this model is good? How can we quantitatively evaluate its performance? How should it be improved?

Let's first discuss the possible error types 👇 Image
Read 12 tweets
18 Nov
Let's talk about a common problem in ML - imbalanced data ⚖️

Imagine we want to detect all pixels belonging to a traffic light from a self-driving car's camera. We train a model with 99.88% performance. Pretty cool, right?

Actually, this model is useless ❌

Let me explain 👇
The problem is the data is severely imbalanced - the ratio between traffic light pixels and background pixels is 800:1.

If we don't take any measures, our model will learn to classify each pixel as background giving us 99.88% accuracy. But it's useless!

What can we do? 👇
Let me tell you about 3 ways of dealing with imbalanced data:

▪️ Choose the right evaluation metric
▪️ Undersampling your dataset
▪️ Oversampling your dataset
▪️ Adapting the loss

Let's dive in 👇
Read 14 tweets
17 Nov
Machine Learning in the Real World 🧠 🤖

ML for real-world applications is much more than designing fancy networks and fine-tuning parameters.

In fact, you will spend most of your time curating a good dataset.

Let's go through the steps of the process together 👇
Collect Data 💽

We need to represent the real world as accurately as possible. If some situations are underrepresented we are introducing Sampling Bias.

Sampling Bias is nasty because we'll have high test accuracy, but our model will perform badly when deployed.

👇
Traffic Lights 🚦

Let's build a model to recognize traffic lights for a self-driving car. We need to collect data for different:

▪️ Lighting conditions
▪️ Weather conditions
▪️ Distances and viewpoints
▪️ Strange variants

And if we sample only 🚦 we won't detect 🚥 🤷‍♂️

👇
Read 16 tweets
16 Nov
Can you detect COVID-19 using Machine Learning? 🤔

You have an X-ray or CT scan and the task is to detect if the patient has COVID-19 or not. Sounds doable, right?

None of the 415 ML papers published on the subject in 2020 was usable. Not a single one!

Let's see why 👇
Researchers from Cambridge took all papers on the topic published from January to October 2020.

▪️ 2212 papers
▪️ 415 after initial screening
▪️ 62 chosen for detailed analysis
▪️ 0 with potential for clinical use

healthcare-in-europe.com/en/news/machin…

There are important lessons here 👇
Small datasets 🐁

Getting medical data is hard, because of privacy concerns, and at the beginning of the pandemic, there was just not much data in general.

Many papers were using very small datasets often collected from a single hospital - not enough for real evaluation.

👇
Read 10 tweets
15 Nov
Mastering your Machine Learning Interview 🧑‍🏫

I've summarized some great resources for you that will help you with your Machine Learning interview.

Read below 👇
A great book by @chipro distilling a lot of information on preparing for a machine learning interview.

huyenchip.com/ml-interviews-…

Next 👇
A collection of questions by @svpino who has a lot of experience interviewing people for ML positions.



Next 👇
Read 9 tweets
12 Nov
How does decentralization help? An example...

The creator and lead dev of the popular NFT exchange Hic Et Nunc on the Tezos blockchain decided to shut down the project. He pulled the plug on the whole website and the official Twitter account.

Yet, the damage is not fatal 👇
How come?

✅ NFTs are fine - they are stored on the blockchain
✅ NFT metadata is fine - stored on IPFS
✅ Exchange backend code is fine - it is in an immutable smart contract
✅ The website is back online - it is open-source, so a clone was deployed by the community fast

👇
Of course, this is a dramatic event and the quick recovery was only possible because of the immense effort of the community. But it is possible and it took basically 1 day.

Imagine the damage that the creator and lead dev could do if they want to destroy a Web 2.0 company!

👇
Read 4 tweets

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