We will be using PyTorch to build a Restricted Boltzmann Machine for a Recommender System Model. PyTorch supports tensors (which are similar to NumPy arrays) & using methods like Contrastive Divergence & Gibbs Sampling can construct robust RBM models. To learn more about how to use PyTorch, go to https://pytorch.org/tutorials/. Also to learn the overall theoretical & mathematical intuition behind RBMs, check out this paper: https://christian-igel.github.io/paper/TRBMAI.pdf.
-
Notifications
You must be signed in to change notification settings - Fork 3
devalindey/Recommender-System-with-RBM-using-PyTorch
Folders and files
| Name | Name | Last commit message | Last commit date | |
|---|---|---|---|---|
Repository files navigation
About
Recommender System Model using Restricted Boltzmann Machine (Energy-based Model)
Topics
Resources
Stars
Watchers
Forks
Releases
No releases published
Packages 0
No packages published