Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.

Boltzmann Machines in TensorFlow with examples

Always sparse. Never dense. But never say never. A 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).

Neural Network Many-Body Wavefunction Reconstruction

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.

Restricted Boltzmann Machines (RBMs) in PyTorch

This repository has implementation and tutorial for Deep Belief Network

Implementation of Restricted Boltzmann Machine (RBM) and its variants in Tensorflow

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

A Julia package for training and evaluating multimodal deep Boltzmann machines

Deep generative models implemented with TensorFlow 2.0: eg. Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN), Deep Boltzmann Machine (DBM), Convolutional Variational Auto-Encoder (CVAE), Convolutional Generative Adversarial Network (CGAN)

algorithm for study: multi-layer-perceptron, cluster-graph, cnn, rnn, restricted boltzmann machine, bayesian network

Fill missing values in Pandas DataFrames using Restricted Boltzmann Machines

A Movie Recommender System using Restricted Boltzmann Machine (RBM), approach used is collaborative filtering.

Implementation of G. E. Hinton and R. R. Salakhutdinov's Reducing the Dimensionality of Data with Neural Networks (Tensorflow)

RBM implemented with spiking neurons in Python. Contrastive Divergence used to train the network.

Deep Learning Models implemented in python.

Simple code tutorial for deep belief network (DBN)

Implementations of (Deep Learning + Machine Learning) Algorithms

Tensorflow Implementation of RBM

Recommend movies to users by RBMs, TruncatedSVD, Stochastic SVD and Variational Inference

Restricted Boltzmann Machines as Keras Layer

Learn Neural Networks using Java

An implementation of Restricted Boltzmann Machine in Pytorch

Restricted Boltzmann Machines implemented in 99 lines of python

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.

Simple Restricted Boltzmann Machine implementation with TensorFlow.

Chapter 4: Basics of Deep Learning