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The Secret ROI Formula: Instant Advertising Efficiency Calculator

Posted: Sun Jan 19, 2025 5:13 am
by Maksudasm
There are several popular neural network architectures, each of which has its own characteristics and its own application for work:

Perceptrons. This is one of the first neural network models, developed by Frank Rosenblatt in 1958. It consists of a single layer of neurons and can adjust weights to correct errors. It is primitive compared to more complex models.

Multilayer neural networks. After the perceptron, it became clear that it had limited ability to recognize objects in non-standard conditions. Multilayer neural networks were developed to extract abstract features from data and solve more flexible problems, such as recognizing objects regardless of lighting or tilt angle.

Recurrent neural networks. They architect data package specialize in working with data such as text, speech, audio, or video. They are able to remember a sequence of data, understand its context, and predict what will happen next. Examples of such neural networks include Google Translate and the voice assistant Alice from Yandex.

Alice (Yandex)

Convolutional neural networks. Designed to work with images. They can perform tasks such as object recognition, image generation, background processing and removal. To do this, they use convolution and pooling algorithms that allow you to highlight important features on different layers of the image.

Generative neural networks. These are capable of creating new data, be it images, texts or other types of content. Examples of such neural networks are the Midjourney and DALL-E image generators, the ChatGPT text data generator and the Lensa selfie processor, which works with images and photographs.

We have presented the most famous neural network architectures that have had a significant impact on the field of artificial intelligence. If you are interested in the full list, you can visit the Asimov Institute's neural network zoo.

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