Відео

smoothing having a similar closed form solution as ridge regression is so satisfying

Lo spirito di generosità e collaborazione qui è incoraggiante. Ci ricorda che siamo più forti insieme.😻

12:14 😬

🔥

It's time to dueel🎉🎉🎉

Lovely presentation, Jimmy

Or where can I get the pptx

Hello . Can you give me the pptx, your topic is interesting, I wanted to get to know it

Waouh😭❤️❤️

Thanks for the video. I will hep next time to have the pointer on the screen as well. It's hard to follow sometimes because we can't see where are you pointing.

90% of my dev work is on Windows and I switched to uv ~6mos ago. Favorite package manager so far, but I also don't create packages for Pypi. I've also always avoided conda after trying it and it just feeling very "heavy." But to each their own, use what works for ya. Nice entertaining talk! 👍

Proud of you Mr SAMEY very good presentation and well explained!

the title of the video (“achieving concurrency in streamlit”) doesn’t match the title of the talk and the description underneath (“open source leadership”)

The code repo please.

Nice video shows methods to create a business..

Great method to viralize contents.

Good presentation ❤

Well, that was meaty!

Great talk Emili. Looking forward to testing these new features.

Nice video shows methods to develop a commercial startup.

Nice video shows methods to create a business.

What about LLMs

Great talk. I will recommend it to newcomers in AI/ML forecasting.

grande Feregrino 🙌

Aren't the constraints restrictive in the sense they're univariate? They're definitely helpful but not exhaustive

Always good to see new players in python streaming ecosystem!

Amazing overview of multi-modal landscape from the practitioner point of view.

very useful speech!

0:00 Introduction 2:10 Workflow >> Tools 2:47 Make's design decisions 5:37 Use Git 6:06 Use Virtual Environments 10:52 Virtual environment name == git repo name 11:34 Check-in your virtual environments 12:00 enviornment.yml 13:31 Script your environment management with "make" 13:57 Makefile 15:30 Never install packages manually 16:58 Use Auto Documentation 19:55 Separate "what you want" from "what you need" 22:28 Don't be afraid to "nuke it from orbit" 23:49 Summary 25:47 Q&A

Learned a lot of paradoxes from Allen!

One question, if we get overfitting propensity scores, then the overlap we want will be very small. It looks like conflict arguments here.

Any idea why the GPU version of this method can't take a pre-computed distance matrix?

Can you please tell me if I want the output as a percentage change, that is If I am varying my input parameter with a 1% change then by how much will my output change using the any of the analysis method that is Sobol or morris?

really neat .. embedded graph.. pretty much creating a graph data struct for programming lang

Nice video shows methods to create a business..

Nice video shows methods to create a business..

very inspiring 😄

clustering is highly driven by the formatting of how the data relates to itself and is near impossible to accomplish using a single method of approach.

is the notebook available online?

Wow - awesome!!!

Bayesian do over-fit a maximum entropy prior will tend to over-fit and encode all the sample noise:)

Great talk!

Thanks for sharing such insightful video, could you please link in the jupyter notebook and the additional notes?

I wish it could lazy scan from a database - the lazy frame function is excellent, but for data security reasons, I’m working from secure database and not parquet files.

Need to slow down.

Can we train a deep learning in an incremental way?

A very impressive presentation and algorithm! Thank you for teaching all this!

Could you possibly make the presenters display a bit smaller, the text is a bit too big and preventing me from admiring the Escher style background

Nice seing "the industry" apply vertical scaling stuff that has been done in games (for world data) for at least the last 20-30 years (since cache fetch fail is order of magnitudes more expensive than a cache normal read and SIMD/MMX intructions are available on x86. I thought that compilers took care of these processor "bowels" stuff since thewlast two decades, and probably they did until now. It seems today the data sets are so huge that we must micromanage memory access and vertical paralelism explicitelly again. For a litle while at least. So it in hindsight, explicit optimisation of code data access patterns was outrunned by processor might, and then was further rendered obsolete by multi-core CPUs: Iin theory, of course, because good usage of parallelism is still in its infancy. But now it seems data set size growth has outrunned CPU growth 🤣 Low level 3D engine game programmers, you have a whole new market opening!

That's a great talk!