Text Classification Algorithms: A Survey

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.

Exploration of various ML models and techniques for cognitive computing tasks. The primary focus is analysing hidden representations and the effectiveness in classifying data

Analysis and implementation of a Deep Belief Network using the Fashion-MNIST dataset.

Deep Belief Networks in Tensorflow 2

Keras framework for unsupervised learning

Energy Based Models in PyTorch

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.

Comparison of DBNs and FFNN, stressing on understanding how DBNs work and how robust they are against noise and adversarial attacks.

A repository for the research article titled "DBNex: Deep Belief Network and Explainable AI based Financial Fraud Detection".

pytorch >>> 快速搭建自己的模型！

用Tensorflow实现的深度神经网络。

Lab assignments for the course DD2437-Artificial neural networks and deep architectures at KTH

Deep Belief Network for Predicting Compound-Protein Interactions

Restricted Boltzmann Machines (RBMs) and Deep Belief Networks (DBNs) from scratch for representation learning on the MNIST dataset.

A collection of some cool deep learning projects in python

Simple code tutorial for deep belief network (DBN)

Seminar report and presentation slides on topic Stochastic Computational Deep Belief Network

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.

A Library for Modelling Probabilistic Hierarchical Graphical Models in PyTorch