The deeplearning algorithms implemented by tensorflow
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Updated
Feb 27, 2019 - Jupyter Notebook
The deeplearning algorithms implemented by tensorflow
Simple framework for image and video deblurring, implemented by PyTorch
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.
GPU accelerated Deep Belief Network
Experimenting with RBMs using scikit-learn on MNIST and simulating a DBN using Keras.
Open DRUWA - Open Deep Realtime User Welcoming Assistant
DBN++ Data Structures and Algorithms in C++ for Dynamic Bayesian Networks
Nebula: Lightweight Neural Network Benchmarks
Classifies images using DBN (Deep Belief Network) algorithm implementation from Accord.NET library
vPaypal provides mobile payment with enhanced security and convenience by using voice recognition and voice control module.The system consists of a mobile app and a server.
Interface between a DBN model and CNN models to learn from demonstrations
The software includes a dynamic bayesian network with genetic feature space selection, includes 5 econometric data.frames with 263 time series.
Tia's implementation of Neural Network Architectures from scratch
📄 Official implementation regarding the chapter "Fine-Tuning Deep Belief Networks with Harmony-Based Optimization".
Simple Keras-inspired DeepLearning Framework implemented in Python with Numpy backend: MLP, CNN, RNN, RBF, SOM, DBN...
A series of 12 assignments/labs regarding Stochastic Processes and Machine Learning including a plethora of models and techniques implemented in Google Colab notebooks
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