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Self Organizing Maps in deep learning

Unsupervised neural network machine learning is used in self-organized maps. https://1stepgrow.com/course/advance-data-science-and-artificial-intelligence-course/

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Self Organizing Maps in deep learning

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  1. Self Organizing Maps (SOMs) In Deep Learning https://1stepgrow.com/course/advance-data-science-and-artificial-intelligence-course/

  2. 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 https://1stepgrow.com/course/advance-data-science-and-artificial-intelligence-course/

  3. 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. https://1stepgrow.com/course/advance-data-science-and-artificial-intelligence-course/

  4. 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. https://1stepgrow.com/course/advance-data-science-and-artificial-intelligence-course/

  5. 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. https://1stepgrow.com/course/advance-data-science-and-artificial-intelligence-course/

  6. 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. https://1stepgrow.com/course/advance-data-science-and-artificial-intelligence-course/

  7. THANK YOU! For Information,Please Visit: https://1stepgrow.com/

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