Radial Basis Function Networks In Deep Learning - PowerPoint PPT Presentation

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Radial Basis Function Networks In Deep Learning

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The Radial Basis Function Neural Networks represent one of the unusual but incredibly quick, efficient, and simple techniques for machine learning. Both classification and regression problems may be solved with the three-layer networks. For more information, Pls visit the 1stepgrow website. – PowerPoint PPT presentation

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Title: Radial Basis Function Networks In Deep Learning


1
Radial Basis Function Networks (RBFNs) In Deep
Learning
https//1stepgrow.com/course/advance-data-science-
and-artificial-intelligence-course/
2
Table Of Content
  1. What Is a RBFNs Algorithm?
  2. Why Is the RBFNs Algorithm Important?
  3. How Does RBFNs Algorithm Work?
  4. Applications Of RBFNs Algorithm

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and-artificial-intelligence-course/
3
What Is a RBFNs Algorithm?
Radial Basis Function Networks may classify data
by comparing input instances to examples from
the training set. The input layer of Radial
Basis Function Networks is fed via an input
vector. These contain a Radial Basis Function
neuron layer. One node per class or data
category is present in the output layer, where
the algorithm calculates the balanced total of
the inputs.
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and-artificial-intelligence-course/
4
Why Is the RBFNs Algorithm Important?
Due to how much the Radial Basis Function Neural
Network Kernel resembles the K- Nearest
Neighborhood Algorithms, it is widely used.
Because Radial Basis Function Neural Network
Kernel Support Vector Machines only need to
store the coordinates while training rather than
the complete dataset, it has the benefits of
K-NN and solves the memory complex issue.
https//1stepgrow.com/course/advance-data-science-
and-artificial-intelligence-course/
5
How Does RBFNs Algorithm Work?
The Radial Basis Function Neural Networks
represent one of the unusual but incredibly
quick, efficient, and simple techniques for
machine learning. Both classification and
regression problems may be solved with the
three-layer networks.
https//1stepgrow.com/course/advance-data-science-
and-artificial-intelligence-course/
6
Applications Of RBFNs Algorithm
  1. Regularization Theory for Radial Basis
    FunctionNetworks
  2. Radial Basis Function Networks for Classification
  3. Supervised Radial Basis Function Network Training
  4. Comparison of Radial Basis Function Networks with
    MLPs
  5. The XOR Problem in Radial Basis Function Form
  6. Real-World Application EEG Analysis

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and-artificial-intelligence-course/
7
THANK YOU!
For Information,Please Visit https//1stepgrow.c
om/
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