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A Derivation and Application of Restricted Boltzmann Machines (2024 Nobel Prize)
A comprehensive explanation of Geoffrey Hinton's work on Restricted Boltzmann Machines (RBMs), covering both theoretical foundations and practical implementation. The article bridges the gap between c
omplex mathematical concepts and code implementation by providing detailed derivations and a complete PyTorch implementation.

Reasons to Read -- Learn:

  • how to implement a foundational deep learning model (Restricted Boltzmann Machine) from scratch in PyTorch, with step-by-step mathematical derivations and code explanations
  • mathematical principles behind Geoffrey Hinton's Nobel Prize-winning work, including energy functions, probability distributions, and contrastive divergence in an accessible way
  • how unsupervised learning works in practice through a concrete example of feature extraction using binary neurons, complete with working code implementation
  • 7 min readauthor: Ryan D'Cunha
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