Post by account_disabled on Feb 18, 2024 4:09:58 GMT
In most cases, neural networks are trained on RGB color images. Pixels transmit brightness through three channels: red, green and blue. Various combinations of these colors give any color from the spectrum. A convolutional neural network classifies images and presents them in a special way - as three-dimensional arrays of numbers or matrices. In mathematics they are called tensors. in the range from 0 to 255. All pixels in the matrix array are represented as numbers that reflect the brightness in this range.
Where are convolutional neural networks used? In the 2010s, artificial Phone Number List convolutional neural networks were widely used in medicine and government agencies for: handwritten text recognition; classification of documents such as SNILS or passport; recognition of neoplasms and other pathologies in images. Currently, convolutional neural networks are used in video surveillance systems, video analytics and self-driving cars. For example, cameras in combination with such networks help Yandex robots and unmanned taxis understand what is happening on the road.
Another popular application of the principle of convolutional neural networks is generative art. When a person enters a query, the neural network generates an image based on it. Text transformation is the task of various algorithms, and image generation is the task of convolutional neural networks. The most common models for image generation are convolutional neural networks such as Midjourney, Stable Diffusion, Dream, DALL-E 2 and ruDALL-E. Neural networks: how useful are they for humanity? Read also Neural networks: how useful are they for humanity? More details Tasks not covered by convolutional neural networks: They are not suitable for analyzing global contexts such as the meaning of a text.
Where are convolutional neural networks used? In the 2010s, artificial Phone Number List convolutional neural networks were widely used in medicine and government agencies for: handwritten text recognition; classification of documents such as SNILS or passport; recognition of neoplasms and other pathologies in images. Currently, convolutional neural networks are used in video surveillance systems, video analytics and self-driving cars. For example, cameras in combination with such networks help Yandex robots and unmanned taxis understand what is happening on the road.
Another popular application of the principle of convolutional neural networks is generative art. When a person enters a query, the neural network generates an image based on it. Text transformation is the task of various algorithms, and image generation is the task of convolutional neural networks. The most common models for image generation are convolutional neural networks such as Midjourney, Stable Diffusion, Dream, DALL-E 2 and ruDALL-E. Neural networks: how useful are they for humanity? Read also Neural networks: how useful are they for humanity? More details Tasks not covered by convolutional neural networks: They are not suitable for analyzing global contexts such as the meaning of a text.