Great content @Dmitriy. I am suppose to deploy a enterprise production level AKS, this video is going to be really resourceful.

This is half baked. You must first determine what kind of data is missing (MCAR, MNAR, MAR) and make the decision to drop/ fillna based on which it is or you must accept the null hypothesis given bad sampling data.

excellent video. the voiceover was better than usual. Would you consider partnering with other engineering channels to grow your audience?

excellent notes and resource!

*fireworks: weeee* *the other firework: AAAAAAAAAAA HELP ME IM GOING TO OUTER SPACEEEEEEEEEEEEEEEEEEEEEE*

finally i found the persion really put high efforts ,, the valuable one ,, thanks bro ,, this is the best vedio i seen in 2024,,

Overall seems somewhat dated: e.g. FTRL-Proximal (2011), DLRM (Meta, 2019) even one year ago Not 100% which parts are still up-to-date & to what extent

Ngl you got me on the first half. I thought you were about to scrape messages and their profiles to analyze the gift they might want using the data 😂

Hey, what do you want for Christmas? Copy paste Excel. Why extra steps? Couse you had to send them link in first place

Learnt a lot on Animation. Thanks Andrei

Not a good answer. You should only remove the 30% if you can verify it is a completely random occurrence that the data is missing. Using the mean to impute also will bias your data or best case if random, just fill it with bad data. The real answer is to build a second model to predict the missing values with all of the same features and output feature to predict the feature with the missing values, then impute the missing values with the prediction, but not with the prediction Irish but randomly with the prediction +/- the prediction error. Now you have a model that is likely not biased by your imputations.

Thanks for sharing

Thanks alot for sharing

AI be taking our jobs now

Best compose animation video i’ve ever seen. Can i have this guy LinkedIn ?

This is awesome lol

Dude at the end thought it was his presentation

Thats really sweet, keep coding!

5 min work won your fair?

How are you hosting the website for public access?

Bruh thats like 10 min work tops (wothout ai i.e if u are already using a api) student in preuniversity make stuff like these for fun

Mentor + ai?

Very informative videos.

ah dude stop recommending ads to me youtube!

Wonderful presentation Is there a link to the slides?

Les conseils sur l'élaboration de SLA pour renforcer la confiance ont été pour moi un point fort. Il est rafraîchissant de voir les SLA considérés comme un outil relationnel, et pas seulement comme une mesure de conformité.

This talk brilliantly demystifies service reliability, showing a seamless path from SLIs to AI-driven excellence. Clear, insightful, and packed with actionable strategies!

Loved the deep dive into SLIs, SLOs, and SLAs! It’s so helpful to see how each metric plays a role in the bigger picture of service reliability and customer satisfaction.

I really appreciated the real-world examples from top companies-it adds so much context to see how AI and machine learning are already transforming service reliability.

Nice Nice discusssion

👍👍👍

Wow, one of the best videos on animation, hope to see more content from this mentor.

Nice Nice discussion

Очень классно всё сказали просто супер

Пост очень понравился большое спасибо

Очень классный пост

❤

Great

Never Skip the fundamentals !

Excellent resource for MLE L5/L6 interview prep !

Genius ho chote bhai. Samai Raina ka show dekhkar aaya hu

Why there is not comments yet? It requires a lot of effort to create video material like this and the efforts has to be appreciated.

Good Video

Incredible! Thanks for sharing.

Binary class- cross entropy loss

Where exactly can I see my cookies profile?

struct ContentView: View { let letters = Array("Hello SwiftUI") @State private var enabled = false @State private var dragAmount = CGSize.zero var body: some View { HStack(spacing: 0) { ForEach(0..<letters.count, id: \.self) { num in Text(String(letters[num])) .padding(5) .font(.title) .background(enabled ? .blue : .red) .offset(dragAmount) .animation(.linear.delay(Double(num) / 20), value: dragAmount) } } .gesture( DragGesture() .onChanged { dragAmount = $0.translation } .onEnded { _ in dragAmount = .zero enabled.toggle() } ) } } I CANNOT DO THIS SIMPLE ANIMATION IN SWIFTUI WITH COMPOSE CODE, CAN YOU HELP ME?

Thanks a lot for the excellent presentation! Quick question, shouldn't we prioritize "precision" over "recall" for the retrieval stage? It was mentioned in the video that recall is more important in the retrieval, however, we should ensure there aren't many FPs in the retrieval stage output, and that would make precision more important.

That's one of the correct ways to learn algos, nice.

Amazing! I subscribed🫶🏾