There are 5 repositories under restricted-boltzmann-machine topic.
Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
Boltzmann Machines in TensorFlow with examples
The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing.
Always sparse. Never dense. But never say never. A Sparse Training repository for the Adaptive Sparse Connectivity concept and its algorithmic instantiation, i.e. Sparse Evolutionary Training, to boost Deep Learning scalability on various aspects (e.g. memory and computational time efficiency, representation and generalization power).
Restricted Boltzmann Machines (RBMs) in PyTorch
This repository has implementation and tutorial for Deep Belief Network
The goal of this project is to solve the task of name transcription from handwriting images implementing a NN approach.
A Library for Modelling Probabilistic Hierarchical Graphical Models in PyTorch
An implementation of Restricted Boltzmann Machine in Pytorch
A Julia package for training and evaluating multimodal deep Boltzmann machines
Implementation of G. E. Hinton and R. R. Salakhutdinov's Reducing the Dimensionality of Data with Neural Networks (Tensorflow)
A Movie Recommender System using Restricted Boltzmann Machine (RBM), approach used is collaborative filtering.
RBM implemented with spiking neurons in Python. Contrastive Divergence used to train the network.
algorithm for study: multi-layer-perceptron, cluster-graph, cnn, rnn, restricted boltzmann machine, bayesian network
Pytorch implementation of an autoencoder built from pre-trained Restricted Boltzmann Machines (RBMs)
Fill missing values in Pandas DataFrames using Restricted Boltzmann Machines
Restricted Boltzmann Machines as Keras Layer
Implementations of (Deep Learning + Machine Learning) Algorithms
Recommend movies to users by RBMs, TruncatedSVD, Stochastic SVD and Variational Inference
Deep Learning Models implemented in python.
Tensorflow Implementation of RBM
Introduction to quantum Monte Carlo. From the foundations to state-of-the-art Restricted Boltzmann Machine ansatz.
Learn Neural Networks using Java
Python implementation of Restricted Boltzmann Machine (RBM). And an example on MNIST dataset.
Implementation of restricted Boltzmann machine, deep Boltzmann machine, deep belief network, and deep restricted Boltzmann network models using python. This code has some specalised features for 2D physics data.
This repository contains all the projects and labs I worked on while pursuing professional certificate programs, specializations, and bootcamp. [Areas: Deep Learning, Machine Learning, Applied Data Science].
Restricted Boltzmann Machines implemented in 99 lines of python
An Numpy implementation of RBM.
Chapter 4: Basics of Deep Learning
In this tutorial, we are going to build a Restricted Boltzmann Machine using TensorFlow that will give us recommendations based on movies that have been watched already. The datasets we are going to use are acquired from GroupLens and contains movies, users, and movie ratings by these users.