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

3328 × 4992

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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,
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
[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.
[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
Introvency
Vulcan water nymphs and space elves and aliens inside spaceship ufo habitat with tentacles
[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
Data selection and cleaning are essential in reducing parameters in a model, especially in neural networks. Selection focuses on relevant and representative samples, reducing dataset size and mitigating overfitting. Cleaning removes noise and inconsistencies, improving data quality and model performance. By selecting informative data, we achieve similar or better results with fewer parameters. Cleaning further simplifies the problem space, reducing dimensionality and improving computational effi
[fractal art: Mandelbulb 3d] dungeon module B3: palace of the silver princess, by Tom Moldvay and Jean Wells (1981) introduction module for characters levels 1-3
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