Placeholder: [in the context of Data Curation and Artificial Intelligence] women and men from the industry thinking together in front of a complex blueprint with flowcharts, they are considering use cases. They are surrounded by cables and data storages [in the context of Data Curation and Artificial Intelligence] women and men from the industry thinking together in front of a complex blueprint with flowcharts, they are considering use cases. They are surrounded by cables and data storages

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[in the context of Data Curation and Artificial Intelligence] women and men from the industry thinking together in front of a complex blueprint with flowcharts, they are considering use cases. They are surrounded by cables and data storages

statue, 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

3 months ago

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[art by Wes Anderson, in the context of Data Curation and Artificial Intelligence] women and men from the industry thinking together in front of a complex blueprint with flowcharts, they are considering use cases. They are surrounded by cables and data storages
women and men from the industry thinking together in front of a complex blueprint with flowcharts
[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.
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 context of Data Curation and Artificial Intelligence] women and men from the industry thinking together in front of a complex blueprint with flowcharts, they are considering use cases
[Tilt-shift photography psychedelic glitched acid trip under steroid] 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, represen
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
people thinking together in front of a blueprint
women and men from the industry thinking together in front of a complex blueprint with flowcharts, they are considering use cases
people from the industry thinking together in front of a complex blueprint with flowcharts
[Tilt-Shift Photography] The circuit board swam into soft focus through the lens, minute details piercing the blurred foreground and background. Golden traces connected components in miniature precision, fibers stretching taut as fairy-line across the substrate. Silicon chips clustered in pleasing arrangement, circuit diagrams etched upon them in intricate patterns too fine for the eye. Mushrooms colonized arrays with pin-prick precision, capped polypores blurring sockets packed with solder ball
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

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