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Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
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Jan 31, 2019 - Python
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rbm
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The deeplearning algorithms implemented by tensorflow
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Feb 27, 2019 - Jupyter Notebook
Boltzmann Machines in TensorFlow with examples
machine-learning
deep-learning
tensorflow
keras
restricted-boltzmann-machine
rbm
dbm
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mcmc
variational-inference
gibbs-sampling
ais
sklearn-compatible
tensorflow-models
pcd
contrastive-divergence-algorithm
energy-based-model
annealed-importance-sampling
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Aug 1, 2020 - Jupyter Notebook
NeuPy is a Tensorflow based python library for prototyping and building neural networks
deep-neural-networks
deep-learning
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tensorflow
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artificial-neural-networks
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deeplearning
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sofm
hopfield
lvq
boltzmann-machine
pnn
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Jan 15, 2020 - Python
Deep Learning Library (DLL) for C++ (ANNs, CNNs, RBMs, DBNs...)
c-plus-plus
machine-learning
performance
cpu
deep-learning
cpp
gpu
cpp14
cpp11
artificial-neural-networks
rbm
convolutional-neural-networks
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Sep 2, 2020 - C++
Tensorflow implementation of Restricted Boltzmann Machine
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Jan 4, 2018 - Python
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Unsupervised learning and generative models in python/pytorch.
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Apr 21, 2020 - Python
Restricted Boltzmann Machines (RBMs) in PyTorch
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Jun 29, 2018 - Python
My solution to TUM's Machine Learning MNIST challenge 2016-2017 [winner]
python
machine-learning
deep-neural-networks
kernel
deep-learning
neural-network
mnist
pca
logistic-regression
rbm
gaussian-processes
data-augmentation
k-nn
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Oct 9, 2019 - Jupyter Notebook
Implementation of Restricted Boltzmann Machine (RBM) and its variants in Tensorflow
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Nov 19, 2017 - Python
adityashrm21
commented
Feb 11, 2019
Check and implement if possible, the use of sparse matrices instead of dense ones so that CUDA doesn't throw Out of Memory error while allocating a large tensor.
Machine Learning Library, written in J
learning
machine-learning
deep-learning
clustering
lstm
gaussian-mixture-models
ensemble-learning
rbm
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j
k-means
gaussian-processes
principal-component-analysis
self-organizing-map
multilayer-perceptron-network
hierarchical-clustering
knn-classifier
restricted-boltzmann-machines
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Feb 11, 2020 - J
Experimenting with RBMs using scikit-learn on MNIST and simulating a DBN using Keras.
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Jul 17, 2017 - Jupyter Notebook
Tensorflow Examples
machine-learning
deep-learning
tensorflow
linear-regression
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autoencoder
logistic-regression
rbm
mlp
nearest-neighbors
multilayer-perceptron-network
tf
grbm
brnn
drnn
sparseae
sparse-autoencoder
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May 11, 2017 - Python
Deep Learning Models implemented in python.
python
deep-learning
sda
models
cnn
restricted-boltzmann-machine
rbm
deeplearning
mlp
multi-layer-perceptron
da
deep-belief-nets
denoising-autoencoders
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Mar 19, 2017 - Python
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Apr 11, 2019 - Python
Restricted Boltzmann Machines for Collaborative Filtering in Tensorflow
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Jul 28, 2017 - Python
Hands-on in-person workshop for Deep Learning with TensorFlow
workshop
deep-learning
neural-network
tensorflow
cnn
lstm
rnn
autoencoder
rbm
recurrent
convolutional
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Sep 2, 2020 - Jupyter Notebook
Tensorflow Implementation of RBM
python
machine-learning
mnist-classification
restricted-boltzmann-machine
rbm
reconstruction
tensorflow-models
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May 4, 2017 - Jupyter Notebook
A regularized version of RBM for unsupervised feature selection.
python
events
machine-learning
data-mining
statistics
tensorflow
embeddings
variable-selection
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rbm
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unsupervised-learning
skipgram
l1
icassp
icassp-2018
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Nov 20, 2019 - Python
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Jul 9, 2018 - Jupyter Notebook
Simple Restricted Boltzmann Machine implementation with TensorFlow.
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May 11, 2018 - Python
An implementation of RNN-RBM & GBRBM.
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Oct 11, 2017 - Python
My python implementation of RBM and denoising Autoencoder for pre-training
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Oct 29, 2016 - Python
Barebones Python implementations of machine learning models, without using machine learning libraries
python
machine-learning
neural-network
clustering
generative-adversarial-network
gan
gaussian-mixture-models
rbm
hidden-markov-model
gmm
gaussian-processes
bayesian-optimization
decision-tree
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Jun 16, 2018 - Python
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Jan 10, 2017 - Python
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It would be helpful to add a tutorial explaining how to run things in parallel (mpirun etc). There are some users who are not familiar with mpi (see #173 ) and it is useful to explain the basic steps to do this.