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the real f(x) daughters btw

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303,992 просмотров • 7 месяцев назад •via X (Twitter)

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F(X)LOV IS REAL F(X)LOV IS REAL WE HAVE XLOV 4 WALLS OH MY GOD
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F(X)LOV IS REAL F(X)LOV IS REAL WE HAVE XLOV 4 WALLS OH MY GOD

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he's her daughters age btw🥀
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he's her daughters age btw🥀

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f(x)’s daughters literally gave us this masterpiece but yall decided to shit on it
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f(x)’s daughters literally gave us this masterpiece but yall decided to shit on it

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Flashback: Anti-Zionist Muslims in London patrol through the capital of England to promote pogrom against Jews: “F**k the Jews... F**k their daughters... F**k their mothers... Rape their daughters... Free Palestine”.
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Flashback: Anti-Zionist Muslims in London patrol through the capital of England to promote pogrom against Jews: “F**k the Jews... F**k their daughters... F**k their mothers... Rape their daughters... Free Palestine”.

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the real center btw
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the real center btw

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the real 2024 foty btw
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the game is real btw
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the real driving simulator btw
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this is the real foty btw
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this is the real foty btw

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Btw I’m beautiful as f
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Btw I’m beautiful as f

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This is the lifestyle you f*king simps are funding btw
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This is the lifestyle you f*king simps are funding btw

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Daughters are the real queens of a father’s heart.❤️
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Daughters are the real queens of a father’s heart.❤️

Dharm Sena 🚩

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Daughters are the real gift given by god 😍❤️
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Daughters are the real gift given by god 😍❤️

Rahul Patel

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same guy btw talking about how daughters of white nationalists are dating j33ts 💔
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same guy btw talking about how daughters of white nationalists are dating j33ts 💔

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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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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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Real fathers & daughters = ❤️
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Real fathers & daughters = ❤️

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real people got it the first time btw
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9. f(x) - 4 Walls 16. f(x) - Shadow 18. Sulli - Dorothy 23. Luna - Free Somebody 25. f(x) - Red Light 44. Sulli - On The Moon 45. f(x) - Rum Pum Pum Pum
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