Mastering your Machine Learning Interview πŸ§‘β€πŸ«

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

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

huyenchip.com/ml-interviews-…

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



Next πŸ‘‡
Machine Learning "Bites" by @rishabh16_

notion.so/Machine-Learni…

Next πŸ‘‡
Some very good advice on how to present yourself in a ML interview by @omarsar0



Next πŸ‘‡
Data science interview questions and answers by the community by @Al_Grigor

ds-interviews.org

Next πŸ‘‡
A big question database on statistics, data science, and machine learning by @mihail_eric and Henry Zhao.

confetti.ai

Next πŸ‘‡
My thread series of machine learning interview questions. Make sure to also look into the comments for some very interesting and creative answers.

Follow me @haltakov for more information and resources on machine learning and computer vision πŸ‘‹

This week I'm reposting some of my best threads from the past months, so I can focus on creating my machine learning course.

Next week I'm back with some new content on machine learning and web3, so make sure you follow me @haltakov.

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More from @haltakov

16 Nov
Can you detect COVID-19 using Machine Learning? πŸ€”

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 πŸ‘‡ Image
Researchers from Cambridge took all papers on the topic published from January to October 2020.

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

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

There are important lessons here πŸ‘‡
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.

πŸ‘‡
Read 10 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 πŸ‘‡
How come?

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

πŸ‘‡
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!

πŸ‘‡
Read 4 tweets
9 Nov
How I made $3000 in 3 weeks selling AI-generated art? πŸ’°

Last week I showed you how you can use VQGAN+CLIP to generate interesting images based on text prompts.

Now, I'll tell you how I sold some of these as NFTs for more than $3000 in less than 3 weeks.

Let's go πŸ‘‡
Background

I've been interested in NFTs for 2 months now and one collection I find interesting is @cryptoadzNFT. What's special about it is that the creator @supergremplin published all of the art in the public domain. This spurred the creation of many derivative projects.

πŸ‘‡
The Idea πŸ’‘

My idea was to use VQGAN+CLIP to create interesting versions of the CrypToadz. So, I started experimenting with my own toad #6741.

I took the original NFT image as a start and experimented a lot with different text prompts. The results were very promising!

πŸ‘‡
Read 20 tweets
8 Nov
Why is AI bad at math? πŸ“

Machine learning models today are good at generating realistic-looking text (see GPT-3), images (VQGAN+CLIP), or even code (GitHub Co-Pilot/Codex).

However, these models only learn to imitate, so the results often contain logical errors.

Thread πŸ‘‡
Simple math problems, like the ones 10-year-old kids solve, usually require several logical steps involving simple arithmetics.

The problem is that, if the ML model makes a logical mistake anywhere along the way, it will not be able to recover the correct answer.

πŸ‘‡
@OpenAI is now working on tackling this issue.

In their latest paper, they introduce the so-called verifiers. The generative model generates 100 solutions, but the verifiers select the one that has the highest chance of being factually correct.

openai.com/blog/grade-sch…

πŸ‘‡
Read 13 tweets
3 Nov
How to create art with Machine Learning? 🎨

You've probably seen these strangely beautiful AI-generated images on Twitter. Have you wondered how they are created?

In this thread, I'll tell you about a method for generating art with ML known as VQGAN+CLIP.

Let's jump in πŸ‘‡
Short History πŸ“œ

In January @OpenAI publicly released CLIP, which is a model that allows matching text to images.

Just days after that, some people like @advadnoun, @RiversHaveWings, and @quasimondo started experimenting using CLIP to guide the output of a GAN using text.

πŸ‘‡
OpenAI published an image generation model together with CLIP, called DALL-E, but without the full code and the pre-trained models.

The results from guiding StyleGAN2 or BigGAN with CLIP aren't as accurate as DALL-E, but they are weirdly artistic.



πŸ‘‡
Read 18 tweets
2 Nov
Creators only get badges πŸ…

There is a problem with how value is distributed in online communities today. It seems we take the status quo for granted and don't discuss it much.

The people that create most of the value, get none of the money! Only badges...

Thread πŸ‘‡
Online communities

I'm talking about platforms like Twitter, Reddit, Stack Overflow etc. They're wonderful places, where you can discuss interesting topics, get help with a problem, or read the latest news.

However, the people that make them truly valuable receive nothing πŸ‘‡
It usually looks like this:

β–ͺ️ Company creates a web 2.0 platform
β–ͺ️ Users create content and increase the value
β–ͺ️ Company aggregates the demand
β–ͺ️ Company monetizes with ads and subscriptions
β–ͺ️ Company gets lots of money
β–ͺ️ Creators get badges, karma and virtual gold

πŸ‘‡ Image
Read 25 tweets

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