A repository for generating synthetic data (images) using various DL/ML models.

A web app for training and analysing Deep Belief Networks

A version of the learnergy package to deal with video datasets

An pytorch implementation of Deep Belief Network with sklearn compatibility for classification. The training process consists the pretraining of DBN, fine-tuning as an unrolled autoencoder-decoder, and supervised fine-tuning as a classifier.

📄 Official implementation regarding the chapter "Fine-Tuning Deep Belief Networks with Harmony-Based Optimization".

AI model example

Simple Keras-inspired DeepLearning Framework implemented in Python with Numpy backend: MLP, CNN, RNN, RBF, SOM, DBN...

Tia's implementation of Neural Network Architectures from scratch

Daft wrapper for easily creating Dynamic Bayesian Network plots

Interface between a DBN model and CNN models to learn from demonstrations

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

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

GPU accelerated Deep Belief Network

The aim of this repository is to create RBMs, EBMs and DBNs in generalized manner, so as to allow modification and variation in model types.

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

Simple framework for image and video deblurring, implemented by PyTorch