Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

F students are inventors (X video)

16,956 Aufrufe • vor 1 Jahr •via X (Twitter)

8 Kommentare

Profilbild von Black Lockhart
Black Lockhartvor 1 Jahr

kracc bacc i have feelings for you

Profilbild von kracc bacc
kracc baccvor 1 Jahr

thank you

Profilbild von 🇵🇸🇮🇱MaoDeBong🇵🇸🇮🇱
🇵🇸🇮🇱MaoDeBong🇵🇸🇮🇱vor 1 Jahr

g

Profilbild von (static tv) 🚫AI but gay
(static tv) 🚫AI but gayvor 1 Jahr

I guess I was just too much of an inventor

Profilbild von WWIA7062
WWIA7062vor 1 Jahr

@grok explain this in JFK terms

Profilbild von Grok
Grokvor 1 Jahr

My fellow students, ask not what your GPA can do for you—ask what you can do with your creativity. Research shows a complex link between grades and innovation. Some studies suggest lower GPAs may signal bolder, unconventional thinking, as students focus less on rigid academic systems. Yet, others find higher GPAs reflect discipline that can fuel creative problem-solving, especially in structured settings. Evidence varies by how we measure creativity—tests often show stronger links to achievement than grades alone. Let us channel our energies, whether our GPA is high or low, into bold ideas that light the path to progress, for innovation knows no bounds.

Profilbild von ImaCanadian
ImaCanadianvor 1 Jahr

Kracc bacc expanding his empire

Profilbild von Red
Redvor 1 Jahr

i edged and exploded immediately!!!

Ähnliche Videos

Do you actually know what convex optimization is in the geometric, guarantee-theoretic sense or have you only met it through solvers and loss curves? Convexity is rare comfort in optimization...there are no spurious local minima, no surprise traps, and inequalities you can use like tools instead of prayers. So, what is this convexity? Let x = (x₁, x₂) and let f(x) be convex. Plot the surface z = f(x). Pick a contact point x₀. The local slope is the gradient p = ∇f(x₀). That p is exactly the data that defines the supporting plane: z = f(x₀) + p · (x − x₀). Thus, f is said to be convex because for every x, f(x) ≥ f(x₀) + p · (x − x₀). So the plane at x₀ can slide under the surface, but it never slices through it. Not near the point...everywhere. Now for here is the interesting part: The slope becomes a coordinate system! Rewrite the same plane as z = p · x − b, where b is the offset. Because the plane passes through (x₀, f(x₀)), the offset is forced to be b = p · x₀ − f(x₀). And that number isn’t just geometry trivia. It’s the convex conjugate: f*(p) = sup over x ( p · x − f(x) ). At a differentiable contact point, the supporting plane touches f tightly enough that the supremum is achieved at x₀, giving the identity f*(p) = p · x₀ − f(x₀) when p = ∇f(x₀). So one moving contact point gives two linked readouts: primal position x₀ dual position (slope) p = ∇f(x₀) dual offset f*(p) One surface. Two worlds. #ConvexOptimization #Optimization #MachineLearning #SignalProcessing #AppliedMath #Engineering

Mathelirium

38,506 Aufrufe • vor 6 Monaten