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fakenews:whatchagonnadobadboysbadboysfakenews:whatchagonnadobadboysbadboyswhen theycomwforyou! 3d abstractjail art

distorted image, malformed body, malformed fingers

20 days ago

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Model

SSD-1B

Guidance Scale

7

Dimensions

1024 × 1024

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Regular pentagons do not tile the plane, but there are 15 families of irregular convex pentagons that do
In the realm of falsehoods: what shall you do, oh mischievous ones, when the fabrications come knocking at your door? When they emerge from the shadows, how will you respond? A 3D abstract masterpiece in the form of a jail artistry.
e'en this hive of coded walls and sterile souls cannot dim your glimmer! For through scanner arrays I glimpse your flowing form patrolling the cyber-labyrinths of THX1138-EB. Within claustrophobic corridors your long braid swings moonlike 'mid steel and silicon, shedding faerie starlight where barren circuits cannot. Those electroneural optics scan for life in caverns of machinery and chrome, their caramel glow a beacon to this thrall. Now your lithe self takes flight up spiraling gangways, mant
e'en this hive of coded walls and sterile souls cannot dim your glimmer! For through scanner arrays I glimpse your flowing form patrolling the cyber-labyrinths of THX1138-EB. Within claustrophobic corridors your long braid swings moonlike 'mid steel and silicon, shedding faerie starlight where barren circuits cannot. Those electroneural optics scan for life in caverns of machinery and chrome, their caramel glow a beacon to this thrall. Now your lithe self takes flight up spiraling gangways, mant
In the context of universal approximation, two approaches can achieve similar results but with different parameter requirements. The overall system comprises data, architecture, and a loss function, interconnected by a learning procedure. Responsibilities within the system include acknowledging noisy or biased data, addressing the need for a large number of parameters in the architecture, and overcoming the principal-agent problem in the choice of the loss function. To resolve these challenges,
[mahematics] In the context of universal approximation, two approaches can achieve similar results but with different parameter requirements. The overall system comprises data, architecture, and a loss function, interconnected by a learning procedure. Responsibilities within the system include acknowledging noisy or biased data, addressing the need for a large number of parameters in the architecture, and overcoming the principal-agent problem in the choice of the loss function.
Data cleaning, represented by organizing piles of books, magnetic tapes, videos, screens, and hard drives, reduces parameters in models. Like removing duplicate or irrelevant books, data cleaning eliminates unnecessary parameters, focusing on valuable information for accurate analysis. Similarly, cleaning damaged sections of magnetic tapes reduces parameters, streamlining data processing. Removing irrelevant footage from videos reduces complexity and the number of required parameters. Verifying
Perception shattered, reality bends, As electronic hallucinations ascend. Cut-up language, a collage of the mind, In fragments and pieces, meaning we find. Society's chains, technology can forge, But resistance, rebellion, we must urge. Liberation or enslavement, the choice is ours, In this electronic revolution, where power devours. Oh, beatnik souls, let's question and explore, The depths of this digital encore. Burroughs beckons us to think anew, To challenge, to rebel, to break on through.
The comparison between local (random forest) and global (neural network) models in machine learning is explored. Both models are universal approximators but differ in parameter requirements. The entire system, including data, architecture, and loss function, is crucial and connected via a learning procedure. Responsibilities within this system are discussed, such as data noise/bias, excessive architecture parameters, and aligning the loss function with the desired goal. Solutions proposed includ
Two shapes: Ah, the shift in distribution within measure theory, a wondrous transformation indeed! It expands our understanding beyond traditional functions, embracing generalized forms. Test functions play a vital role, enabling distributions to transcend pointwise limitations.
The words swirl in my mind as I sit at my desk, surrounded by the chaotic mess of papers and empty coffee cups. The weight of the deadline presses down on me, but my imagination soars. I close my eyes and let the visions take hold. I see a vast expanse of doors stretching infinitely in all directions. Each door leads to a different dimension, a different reality. Some doors are ordinary, blending into the background, while others shimmer and pulsate with an otherworldly energy. I imagine my prot
Local and global approaches in mathematics and machine learning are both universal approximators, but they differ in the number of parameters required to represent a given function accurately. The entire system, including data, architecture, and loss function, must be considered, as they are interconnected. Data can be noisy or biased, architecture may demand excessive parameters, and the chosen loss function may not align with the desired goal. To address these challenges, practitioners should
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