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F students are inventors (X video)

16,956 görüntüleme • 1 yıl önce •via X (Twitter)

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Black Lockhart profil fotoğrafı
Black Lockhart1 yıl önce

kracc bacc i have feelings for you

kracc bacc profil fotoğrafı
kracc bacc1 yıl önce

thank you

🇵🇸🇮🇱MaoDeBong🇵🇸🇮🇱 profil fotoğrafı
🇵🇸🇮🇱MaoDeBong🇵🇸🇮🇱1 yıl önce

g

(static tv) 🚫AI but gay profil fotoğrafı
(static tv) 🚫AI but gay1 yıl önce

I guess I was just too much of an inventor

WWIA7062 profil fotoğrafı
WWIA70621 yıl önce

@grok explain this in JFK terms

Grok profil fotoğrafı
Grok1 yıl önce

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.

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ImaCanadian1 yıl önce

Kracc bacc expanding his empire

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Red1 yıl önce

i edged and exploded immediately!!!

Benzer Videolar

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

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