Restricted Boltzmann Machines( RBMs) in deep learning.pptx

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Restricted Boltzmann Machines( RBMs)
In Deep Learning
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Table Of Content

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

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What Is a RBMs Algorithm?
The Restricted Boltzmann Machine is the name of
the artificial neural network which makes up the
power-based models. It is a probable, generative
nature, unsupervised deep learning algorithm.
The restricted Boltzmann Machine seeks to
identify a joint distribution of probabilities
that increases the logarithmic likelihood value.
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Why Is the RBMs Algorithm Important?
In the age of deep learning and Machine
Learning, the Restricted Boltzmann Machine
technology, used for choosing features and
extracting features, is essential for reducing
dimensionality, classification, regression, and
many other applications.
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How Does RBMs Algorithm Work?
In extra to the input portion of the layer,
commonly referred to as the visible or hidden
layer, the Boltzmann Machine also has one or
more hidden layers. The neural networks used by
the Boltzmann Machine have neurons connected to
neurons in the same layer and neurons in other
layers. Everything in the universe is related in
every way.
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Applications Of RBMs Algorithm

• Video and Image processing
• Natural language processing
• Risk Detection.
• Industry and Economic analysis.
• Financial modelling
• Bioinformatics
• Classification.
• Collaborative filtering
• Anomaly detection
• Filtering.
• Feature Learning.
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