15 terms you need to know to work with neural networks

Modern technologies develop quickly. Artificial intelligence has already entered into almost all areas of our lives. Some have introduced it to their work, others use it to solve daily problems.

But still not everyone understands how to work with a virtual assistant. Therefore, we offer to meet the main terms that will help you find a common language with the nervous network and make your interaction more effective.

Model – A specific version of the neural network. For example, Deepseek R1-Zero and Deepseek R1.

Artificial intelligence – A system that mimics a person’s cognitive functions, self -training and search for solutions without a predetermined algorithm.

Plexus – A program that repeats the human neural connections model and works on the basis of artificial intelligence.

Neuron – The basic element of the nervous network that accepts and transmits information.

Productive and formed nerve network One of the two chapters in which one is responsible for creating content, and the other for analysis, as a result, delete poor generations. This contributes to the training of both participants in this process.

Diffusion nerve network It can produce images of random noise. It slowly adds noise to the data and then stops this process to create new, high quality data: text, images or sound.

Multi -mod nervous network Competence of different data types: text, images, audio, video. Thanks to the ability to process a large amount of data, they can solve various problems.

Data set – Data on which the model is trained: A series of texts, images, video, sound, etc. The resulting result depends on the cluster of the neural network. For example, if it is taught in the video clips of Russian stars, it will write a similar song.

To introduce – The task or question that arises by neural networks.

Drink – The process in which artificial intelligence fills with new data that combines the absence of the image in accordance with the existing elements.

Termination – The process in which artificial intelligence adds new elements by expanding the limits of the image. In this case, AI should understand the style and content of the original picture.

Hallucinations – Failures of the nervous network that leads to the wrong solution to the task.

Machine Training of Neural Network – Creating algorithms that allow computers to learn to analyze data to perform specific tasks. The more examples of computer operations, the better what to do.

Deep Education of Nervous Network – The algorithms themselves learn to identify molds and find general features based on loaded data.

Ways to interact with neural networks (Text-Image, Metin-Metin, Text-Video, etc.)-Concepts consisting of two parts. The first word is a request, the second is the expected result.

We’ve talked about the functions of the nervous networks that scared before.