Sharm Fernando

Student

[Drew @Drewdog919]

I have watched this at least 25 times today. Simply the greatest walkout in sports history

[Truthseeker @Xx17965797N]

What you're actually permitting when clicking "I'm not a robot"

[Robert Scoble @Scobleizer]

Exclusive to X:

I visit @factoryAI's CEO @matanSF to learn about the future of software engineering.

San Francisco's best new AI developer tool.

It isn't like Visual Basic of the past.

For one, you don't write much code.

$40 a month. Incredible.

[Riley Brown @rileybrown]

The fastest way to create a mobile app with cursor:

1. Prototype on phone
2. Export to Cursor
3. Make Changes on Cursor while testing on your phone

Timestamps:
00:00 Intro
00:30 Prototype on @v_computer
01:06 App Version 1
01:29 Making First Edit
03:06 Exporting to @cursor_ai
05:50 Making Logo for app in ChatGPT and uploading to Cursor

[Dhaval Makwana @heyDhavall]

🚨BREAKING: This AI tool just killed video shoots.

Upload a product pic → get ad-ready influencer videos at 1/10th the cost.

No models. No cameras. No editors.

Here’s how it works:

[Ian Miles Cheong @ianmiles]

Milton Friedman describes the pencil, a product like any other that’s the combined result of free market economics and complex supply chains—and not central planning.

[mark Kacy @mark_kacy]

Website for orphans 😭

[Sharm Fernando @SharmFernando33]

I just published Mastering SOLID Principles in Software Engineering: Examples, Best Practices, and Interview insights
https://t.co/aS9HrZKIw0
#SoftwareDevelopment #SoftwareEngineering

[The Founders Club @FoundersClubUS]

I recently read about Burger King’s legendary marketing stunt.

Ever since, I haven’t been able to stop thinking about it.

Here’s what they did:

[Sharm Fernando @SharmFernando33]

@ravinhwg Very true 🤣🤣

[Azzam Ameen @AzzamAmeen]

🏆 Historic Victory! Sri Lanka women's team clinches their first ever Asia Cup title, defeating India 🇱🇰🏏

[Josh Gessner @joshgessner]

Most people have horrible flexibility.

So, I turned to the science behind muscle flexibility.

What I found?

The Optimal Stretching Protocol:

[Sharm Fernando @SharmFernando33]

I just published React Fiber Explained: Incremental Rendering for Smoother UIs https://t.co/xcjYnFtlmh

#react
#reactfiber
#webdevelopment

[Matt Dancho (Business Science) @mdancho84]

Principal Component Analysis (PCA) is the gold standard in dimensionality reduction with uses in business. In 5 minutes, I'll teach you what took me 5 weeks. Let's go!

1. What is PCA?: PCA is a statistical technique used in data analysis, mainly for dimensionality reduction. It's beneficial when dealing with large datasets with many variables, and it helps simplify the data's complexity while retaining as much variability as possible.

2. How PCA Works: PCA has 5 steps; Standardization, Covariance Matrix Computation, Eigen Vector Calculation, Choosing Principal Components, and Transforming the data.

3. Standardization: The first step in PCA is to standardize the data. Since the scale of the data influences PCA, standardizing the data (giving it mean of 0 and variance of 1) ensures that the analysis is not biased towards variables with greater magnitude.

4. Covariance Matrix Computation: PCA looks at the variance and the covariance of the data. Variance is a measure of the variability of a single feature, and covariance is a measure of how much two features change together. The covariance matrix is a table where each element represents the covariance between two features.

5. Eigenvalue and Eigenvector Calculation: From the covariance matrix, eigenvalues and eigenvectors are calculated. Eigenvectors are the directions of the axes where there is the most variance (i.e., the principal components), and eigenvalues are coefficients attached to eigenvectors that give the amount of variance carried in each Principal Component.

6. Choosing Principal Components: The eigenvectors are sorted by their eigenvalues in descending order. This gives the components in order of significance. Here, you decide how many principal components to keep. This is often based on the cumulative explained variance ratio, which is the amount of variance explained by each of the selected components.

7. Transforming Data: Finally, the original data is projected onto the principal components (eigenvectors) to transform the data into a new space. This results in a new dataset where the variables are uncorrelated and where the first few variables retain most of the variability of the original data.

8. Evaluation: Each PCA component accounts for a certain amount of the total variance in a dataset. The cumulative proportion of variance explained is just the cumulative sum of each PCA's variance explained.

There you have it- my top 6 concepts on PCA. The next problem you'll face is how to apply data science to business.

I'd like to help.

I’ve spent 100 hours consolidating my learnings into a free 5-day course, How to Solve Business Problems with Data Science. It comes with:

300+ lines of R and Python code
2 systematic frameworks
1 complete roadmap to avoid mistakes and start solving business problems with data science, TODAY.

👉 Here it is for free: https://t.co/YXG4pL97ZN

[DAZN Boxing @DAZNBoxing]

OLEKSANDR USYK HAS BEAT TYSON FURY AND IS UNDISPUTED HEAVYWEIGHT CHAMPION! 😱👑

#FuryUsyk | #RingOfFire | #RiyadhSeason | @Turki_alalshikh | @usykaa

[Market News🇱🇰 @MarketNewsLK]

"I wanna tell you a story, Americans love a comeback story, and in the region I work on, there's no greater comeback story, than the story of #SriLanka" -United States Assistant Secretary of State for South & Central Asian Affairs, Donald Lu
🇱🇰👏🏼