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

The deeplearning algorithms implemented by tensorflow

Boltzmann Machines in TensorFlow with examples

NeuPy is a Tensorflow based python library for prototyping and building neural networks

Deep Learning Library (DLL) for C++ (ANNs, CNNs, RBMs, DBNs...)

Tensorflow implementation of Restricted Boltzmann Machine

Unsupervised learning and generative models in python/pytorch.

Restricted Boltzmann Machines (RBMs) in PyTorch

My solution to TUM's Machine Learning MNIST challenge 2016-2017 [winner]

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

Machine Learning Library, written in J

Experimenting with RBMs using scikit-learn on MNIST and simulating a DBN using Keras.

Tensorflow Examples

💡 Learnergy is a Python library for energy-based machine learning models.

Deep Learning Models implemented in python.

https://github.com/mehulrastogi/Deep-Belief-Network-pytorch

Restricted Boltzmann Machines for Collaborative Filtering in Tensorflow

Hands-on in-person workshop for Deep Learning with TensorFlow

Tensorflow Implementation of RBM

Train a DBN to classify a set of test data similar to MNIST, Using DL4J & theano (Project of Pattern Recognition course)

A regularized version of RBM for unsupervised feature selection.

🔎 Neural Network Quantum State Tomography (based on https://www.nature.com/articles/s41567-018-0048-5)

Simple Restricted Boltzmann Machine implementation with TensorFlow.

An implementation of RNN-RBM & GBRBM.

My python implementation of RBM and denoising Autoencoder for pre-training

Barebones Python implementations of machine learning models, without using machine learning libraries

An introduction of MCMC, implementation of TAP, AIS, RTS algorithm to estimate the partition function of an RBM (Project of Stochastic Process course)