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dimensionality-reduction
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Practice and tutorial-style notebooks covering wide variety of machine learning techniques
flask
data-science
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statistics
deep-learning
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random-forest
clustering
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naive-bayes
scikit-learn
regression
pandas
artificial-intelligence
pytest
classification
dimensionality-reduction
matplotlib
decision-trees
k-nearest-neighbours
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May 23, 2021 - Jupyter Notebook
A curated list of community detection research papers with implementations.
data-science
machine-learning
deep-learning
social-network
clustering
community-detection
network-science
deepwalk
matrix-factorization
networkx
dimensionality-reduction
factorization
network-analysis
unsupervised-learning
igraph
embedding
graph-clustering
node2vec
network-clustering
bigclam
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Jun 24, 2021 - Python
Community-curated list of software packages and data resources for single-cell, including RNA-seq, ATAC-seq, etc.
python
bioinformatics
analysis
clustering
gene-expression
data-visualization
dimensionality-reduction
awesome-list
data-integration
atac-seq
single-cell
rna-seq-data
scrna-seq-data
cell-cycle
cell-differentiation
gene-expression-profiles
analysis-pipeline
cell-populations
rna-seq-experiments
cell-clusters
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Jul 27, 2021
Text Classification Algorithms: A Survey
deep-learning
random-forest
text-classification
recurrent-neural-networks
naive-bayes-classifier
dimensionality-reduction
logistic-regression
document-classification
convolutional-neural-networks
text-processing
decision-trees
boosting-algorithms
support-vector-machines
hierarchical-attention-networks
nlp-machine-learning
conditional-random-fields
k-nearest-neighbours
deep-belief-network
rocchio-algorithm
deep-neural-network
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Apr 9, 2021 - Python
machine-learning
clustering
som
neural-networks
dimensionality-reduction
outlier-detection
unsupervised-learning
manifold-learning
self-organizing-map
vector-quantization
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Jul 7, 2021 - Python
Extensible, parallel implementations of t-SNE
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Jun 6, 2021 - Python
A repository of pretty cool datasets that I collected for network science and machine learning research.
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benchmark
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community-detection
network-science
deepwalk
dataset
dimensionality-reduction
network-analysis
network-embedding
link-prediction
gcn
node2vec
graph-embedding
node-classification
graph2vec
node-embedding
graph-convolution
gnn
graph-neural-network
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May 8, 2021
Minimum-distortion embedding with PyTorch
visualization
machine-learning
gpu
cuda
pytorch
dimensionality-reduction
embedding
graph-embedding
feature-vectors
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Jul 3, 2021 - Python
Using siamese network to do dimensionality reduction and similar image retrieval
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Jul 22, 2019 - Jupyter Notebook
An R package implementing the UMAP dimensionality reduction method.
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Jun 5, 2021 - R
Dimensionality reduction in very large datasets using Siamese Networks
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Jul 15, 2021 - Python
Machine Learning notebooks for refreshing concepts.
python
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natural-language-processing
reinforcement-learning
deep-learning
machine-learning-algorithms
neural-networks
deep-learning-algorithms
dimensionality-reduction
python-machine-learning
data-processing
regression-models
deep-learning-tutorial
data-science-notebook
model-evaluation
classification-trees
clustering-methods
machine-learning-tutorials
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Oct 31, 2018 - Jupyter Notebook
A Julia package for multivariate statistics and data analysis (e.g. dimension reduction)
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Jun 3, 2021 - Julia
JavaScript implementation of UMAP
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Oct 14, 2020 - JavaScript
yuki-koyama
opened
Apr 19, 2018
A sparsity aware implementation of "Deep Autoencoder-like Nonnegative Matrix Factorization for Community Detection" (CIKM 2018).
data-science
machine-learning
deep-learning
clustering
word2vec
sklearn
community-detection
deepwalk
autoencoder
dimensionality-reduction
unsupervised-learning
cikm
embedding
nmf
coordinate-descent
node2vec
node-embedding
gemsec
mnmf
danmf
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Jul 27, 2021 - Python
An implementation of demixed Principal Component Analysis (a supervised linear dimensionality reduction technique)
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Jul 30, 2020 - Jupyter Notebook
t-Distributed Stochastic Neighbor Embedding (t-SNE) in Go
visualization
go
data-science
machine-learning
dimensionality-reduction
unsupervised-learning
tsne
3d
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Jul 8, 2020 - Go
Codes and Project for Machine Learning Course, Fall 2018, University of Tabriz
python
machine-learning
clustering
linear-regression
regression
neural-networks
supervised-learning
pca
classification
dimensionality-reduction
logistic-regression
recommender-system
gradient-descent
support-vector-machines
backpropagation
anomaly-detection
unsupervised-machine-learning
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Jan 10, 2019 - Jupyter Notebook
A New, Interactive Approach to Learning Data Science
python
machine-learning
random-forest
regression
datascience
dimensionality-reduction
feature-engineering
data-preparation
machine-learning-pipelines
binaryclassification
clusteranalysis
hyperparameter-tuning-
ensemble-learning-
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Feb 26, 2021 - Jupyter Notebook
[Under development]- Implementation of various methods for dimensionality reduction and spectral clustering implemented with Pytorch
pytorch
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graph-cut
diffusion-maps
pytorch-tutorial
diffusion-distance
laplacian-maps
fiedler-vector
pytorch-demo
pytorch-numpy
sorting-distance-matrix
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Oct 6, 2017 - Python
Deep Learning sample programs using PyTorch in C++
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image-to-image-translation
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dagmm
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Jul 25, 2021 - C++
python
markov-model
hmm
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msmbuilder
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tica
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Jan 26, 2021 - Python
Parametric UMAP embeddings for representation and semisupervised learning. From the paper "Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning" (Sainburg, McInnes, Gentner, 2020).
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Feb 8, 2021 - Jupyter Notebook
Uniform Manifold Approximation and Projection - R package
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Dec 10, 2020 - R
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Apr 14, 2021 - Jupyter Notebook
Python library for Self-Organizing Maps
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Jun 15, 2021 - Python
Introduction to manifold learning - mathematical theory and applied python examples (Multidimensional Scaling, Isomap, Locally Linear Embedding, Spectral Embedding/Laplacian Eigenmaps)
python
dimensionality-reduction
manifold-learning
isomap
multidimensional-scaling
spectral-embedding
laplacian-eigenmaps
locally-linear-embedding
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Mar 5, 2020 - Jupyter Notebook
Ensemble topic modelling with pLSA
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Mar 30, 2021 - Python
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Following up on the discussion here, it would be good to document how to get reproducible results with UMAP.
I think we should consider changing
random_state
in the UMAP constructor to a seed (e.g. 42, like the newtransform_seed
default) so that UMAP is reproducible by default.We should document that users can set `ran