Vladimir U

[Vladimir U @uspenskiyv]

Статистическая обработка и бесплатные приложения для анализа биомедицинских данных - теперь больше информации вы можете найти на сайте "Научный редактор" в разделах "Обработка научных данных" и "Полезные ресурсы".

Сбор и анализ научных данных: https://t.co/7XCWXIhXlH #статистика

[The Nobel Prize @NobelPrize]

A round of applause!

Students and colleagues at @lunduniversity came together to recognise newly named physics laureate Anne L’Huillier.

Drop a 👏 in the comments to congratulate our new Nobel Prize laureate.

Video credit: Nina Ransmyr, Lund University

[Vladimir U @uspenskiyv]

@nori_acami

[Vladimir U @uspenskiyv]

Запятую пропустили

[Michael Pyrcz🌻 @GeostatsGuy]

I taught principal components analysis, PCA, in my #MachineLearning course yesterday. Some students were curious about the component loadings!

Last night I coded this interactive PCA demonstration with @matplotlib. Change the data and watch the variance explained and loadings change for each principal component! Stoked!

Check it out on #GitHub @ https://t.co/8kuicemoFO ∀.

[MIT CSAIL @MIT_CSAIL]

All major neural networks, in one chart: https://t.co/F6da67oQc4

v/The Asimov Institute

[Kareem Carr, Ph.D. @kareem_carr]

I'm reading Elon Musk's biography.

One revelation was more fascinating to me than all of the gossip combined.

It's what the book calls The Algorithm. Elon's simple 5 step recipe for success. Read on to learn all about the algorithm and what it implies for the future of Twitter.

The Algorithm

The five steps of the algorithm are:

1. Question every requirement. Each should come with the name of the person who made it. You should never accept that a requirement came from a department, such as from “the legal department” or “the safety department.” You need to know the name of the real person who made that requirement. Then you should question it, no matter how smart that person is. Requirements from smart people are the most dangerous, because people are less likely to question them. Then make the requirements less dumb.

2. Delete any part or process you can. You may have to add them back later. In fact, if you do not end up adding back at least 10% of them, then you didn’t delete enough.

3. Simplify and optimize. This should come after step two. You should avoid doing this for parts and processes that should not exist.

4. Accelerate. Every process can be sped up. But only do this after you have followed the first three steps. Again, you should avoid doing this for parts and processes that should not exist.

5. Automate. This comes last. Wait until all requirements have been questioned, parts and processes deleted, and bugs removed.

This process feels potentially very powerful to me. I'm interested to try it out in my own life and to see how it works. If we follow the advice of the algorithm itself, then we should not accept it just because it works for the richest man in the world. We should each vet it for ourselves.

The Future of Twitter

It was clear when Elon bought Twitter that he was questioning a lot of the received wisdom. People wondered if he was crazy or stupid and why he wasn't listening to experts. That seems to have been the questioning-every-requirement phase.

He followed up that stage by deleting as much as he could. Almost all of the employees most spectacularly. A lot of people noted that he'd probably end up realizing he needed a lot of what he was cutting. Apparently that was to be expected and a key part of his process.

As far as I can tell, looking in from the outside, we are probably now in the simplification and optimization phase. Hopefully this means things will be settling down for Twitter in the foreseeable future.

Hope you found this commentary on Elon's biography helpful.

I 'm currently tweeting insights from another book called The Book of Why which teaches us about the new science of causation.

Follow me (@kareem_carr) to get data science threads in your timeline 2-3 times a week.

[Vladimir U @uspenskiyv]

@annMJJ Вот если бы это была BMW…

[Dr Raymond C Lee MD @drraycleemd]

A little slice of history during this last case. Never thought I would see one of these. Name that valve @tomcnguyen @EcmoNinja @SergeKobsaMD @koriannj @UscCardiac @USCHeartFailure @AortaSurg @PeterDowneyMD @LuisCastroMD

[Vladimir U @uspenskiyv]

✅ Bentall procedure through an upper J-shaped ministernotomy
The standard access to the ascending aorta is a full midline sternotomy. An alternative access to the ascending aorta and aortic valve is an upper J-shaped sternotomy. #bav #aorta #aneurysm #minimallyinvasive #bentall

[Vladimir U @uspenskiyv]

Для самых маленьких любителей пива

[Vladimir U @uspenskiyv]

Хочешь бесплатную книжку - найди ссылку

[Python Coding @clcoding]

Pdf To Audio using Python https://t.co/E2y2SKFPgC

[Madza 👨‍💻⚡ @madzadev]

5 FREE Coding Courses by Stanford University 🔥🔥

1. Internet of Things
https://t.co/iVWhxzvBWy

2. Computer Science
https://t.co/pQNgw47tfO

3. Databases
https://t.co/A3JmK4gyFQ

4. Statistics
https://t.co/KtirwPWw7n

5. Machine Learning
https://t.co/p0CMlsUOpW

[Giambattista La Forgia @Giam00]

Marfan 28yo aortic root aneurism + severe mitral insufficiency for Barlow disease. Surgical treatment w/ mitral band + p2 resection and David procedure @CuoreVzla @crtorrealba @rafaelnor @OPreventzaMD @AortaSurg @JCoselli_MD

[Edward Bender, MD @ebender001]

Assumes the septal leaflet is not the infected one.

[Kirk Borne @KirkDBorne]

Understanding Encoder and Decoder #LLMs: https://t.co/0ydbMemZCN by @rasbt
+
What's a Transformer? #ChatGPT explains that in Example #7 here: https://t.co/ZqXO5MnZ9A
======
#AI #DeepLearning #MachineLearning #Transformers #NLProc #GPT3 #GPT4 #BigData #DataScience #NeuralNetworks

[Kirk Borne @KirkDBorne]

Download 244-page PDF “Introduction to #NeuralNetworks”
https://t.co/CWVdswSt7H via @MikeE_3_14
————
#BigData #DataScience #AI #Algorithms #MachineLearning #DeepLearning

[Python Coding @clcoding]

Remove Image Background using Python

[Akshay 🚀 @akshay_pachaar]

Two of the fundamental evaluation metrics in machine learning are:

- Precision
- Recall

But, their formal definitions can be a bit confusing.

Today, I'll clearly explain precision & recall using illustrative examples! 🚀

A Thread 🧵👇