.The use of neural networks to predict

發表於 2023-7-24 17:19:07 | 顯示全部樓層 |閱讀模式
A selection of 50+ IT resources Only the best telegram channels, Youtube channels, podcasts, forums and much more to learn new things about IT pdf icon TOP 50+ services and applications from Geekbrains Safe and reliable programs for work today Get a selection for free Click animation pdf 3,7mb doc 1,7mb Already downloaded 21776 pdf icon Clustering A large number of input signals with previously unknown features are divided into classes by a trained neural network. It independently determines where exactly to attribute the input signal, and also reports that it does not correspond to the characteristics of any of the available classes, that is.
Outdated by the current moment. As far as the classes Buy Cell Phone Number List highlighted by the network correspond to those that really exist in the sub ject area, a person establishes. To solve the clustering problem, for example le, Kohonen's neural networks are designed. Prediction and Approximation the next steps in the development of various processes is a direct consequence of the ability of AI to analyze large amounts of in formation, find patterns and make predictions based on the results of information processing .It should be borne in mind that forecasting using a neural network makes sense in cases where previous actions and phenomena really affect subsequent ones.

For example you can predict the dynamics of sales volumes by months, based on data over the past few years. Traders use NN to assess new opportunities and build trading strategies: by analyzing the characteristics of successful companies,the neural network predicts which newcomers are most likely to show similar growth in the foreseeable future. Data compression and associative memory Neural networks reveal the relationship between various parameters, therefore, based on the identified patterns, they are able to scale the data, bringing them into a compact form. And, conversely, they can restore data in full, based on the information obtained from the analysis of the available fragment.


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