Self Organizing Maps in deep learning

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Self Organizing Maps (SOMs)
In Deep Learning
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Table Of Content

1. What Is a SOMs Algorithm?
2. Why Is the SOMs Algorithm Important?
3. How Does SOMs Algorithm Work?
4. Applications Of SOMs Algorithm

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What Is a SOMs Algorithm?
Unsupervised neural network machine learning is
used in self-organized maps. Self-organising
Maps are used whenever a data set contains many
characteristics the result is often two
dimensions and low dimensions. The result, known
as the Map, is a separate model of the input
area.
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Why Is the SOMs Algorithm Important?
Self-Organizing Maps for Visualizing Data,
Clustering, which is and Dimension Reduction.
One of the famous unsupervised neural network
models is the self-organizing Map. Clustering,
reducing the dimension, and identifying features
have extensively used Self-Organizing Maps.
Professor Kohonen is the one who initially
described the Self- Organizing Map.
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How Does SOMs Algorithm Work?
We can visualize big datasets and locate
possible clusters using self-organizing maps,
which are grids of neurons that adjust to the
topological form of the data set. By constantly
bringing its neurons nearer to the data points,
a SOM learns the form of a dataset.
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Applications Of SOMs Algorithm
It is commonly used in various application
fields, including text mining, sociology,
geographical areas, industry, commerce, and
management. Numerous variations have been
developed for processing complicated data,
including time series, categorical data, nominal
data, difference, or kernel data.
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