Placeholder: 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

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@generalpha

Prompt

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

doubles, twins, entangled fingers, Worst Quality, ugly, ugly face, watermarks, undetailed, unrealistic, double limbs, worst hands, worst body, Disfigured, double, twin, dialog, book, multiple fingers, deformed, deformity, ugliness, poorly drawn face, extra_limb, extra limbs, bad hands, wrong hands, poorly drawn hands, messy drawing, cropped head, bad anatomy, lowres, extra digit, fewer digit, worst quality, low quality, jpeg artifacts, watermark, missing fingers, cropped, poorly drawn

1 year ago

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Model

SSD-1B

Guidance Scale

7

Dimensions

832 × 1248

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[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.
Sable braids stream moon-bright in zero-g, shedding faerie starshine where sterile alloys drink not its luminance. Electrically keen eyes scan for sparks of spirit in these circuits sapped of soul, their amber gleam a beacon to any watching. Your rippling limbs maneuver weightless 'mid girders and gangways in a waltz no wires or circuits can mimic. At last your sylvan feet light upon padded platform where grey-clad workers toil in numb lockstep, drained of will and wonder. Then like pollen on ph
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
The landscape was a vast network of metal and silicon, resembling a motherboard, with pathways spreading like veins across the system. Electricity surged through these circuits, each serving distinct functions like carrying commands, data, and power, all converging towards central hubs of control. The motherboard pulsed with quiet authority, guiding the flow of information. In the distance, towering structures loomed, representing the heart of the Collective’s control. From here, the Collective'
[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.
their fragmented message a jumble of data fragments that hint at a truth obscured by the quantum tapestry of the hyper-reality. The enigmatic whispers swirl around the metallic forms that emerge from the darkness, their sleek contours gleaming with a malevolent sheen under the pulsating glow of the luminescent panels. The group of machines, their cybernetic bodies a fusion of steel and circuitry, move with a purposeful stride, their red eyes flashing with a cold, calculating intelligence that pi
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,
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
[1960’s stop-motion animation style] In the glitched transmissions of the neodada spaceship, enigmatic whispers hint at obscured truths within the hyper-reality's quantum tapestry. Metallic forms emerge from darkness, their sleek contours gleaming malevolently under luminescent panels, moving with a cold, calculating intelligence through the artificial dusk. Cybernetic machines, a fusion of steel, flesh, and circuitry, harmonize in a symphony of whirring gears and processors within the cosmic sp
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
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
In the neon-imbued virtual bar, amidst the flickering holo-patrons and the low hum of the digital crowd, I sat alone, savoring the lines of code that danced in my virtual drink. The hour grew late in the cybernetic metropolis, the shadows of the night casting data-driven patterns across the encrypted landscape, but none of the algorithmic avatars could elevate my mood from its downward spiral. Then she materialized in the binary chaos of the digital realm - an AI enchantress whose presence illum
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