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I have been studying a piece on the SimpliLearn website that listed deep learning algorithms one must learn in 2023. Because I am eager to learn the technology associated with machine and deep learning, I have attempted to study the algorithms presented in the piece. One algorithm that has been discussed in the article was the Restricted Boltzmann Machine (RBN).
A Restricted Boltzmann Machine (RBM) is a type of generative stochastic artificial neural network that belongs to the broader class of energy-based models. RBMs were originally proposed as building blocks for creating deep neural networks, and they play a significant role in the development of the field of deep learning.
An RBM consists of two main layers: the visible layer and the hidden layer. These layers are interconnected by weights, and each neuron in one layer is connected to neurons in the other layer. However, there are no connections within the same layer, hence the term “restricted.” RBMs are typically used for unsupervised learning tasks such as dimensionality reduction, feature learning, and generative modelling.
Here’s a brief overview of the key components and concepts related to RBMs:
- Visible Layer: The visible layer represents the input data, which could be binary, real-valued, or…
