21 Open source projects that are alternatives of or similar to Embeddings2Image

R wrappers to connect Python dimensional reduction tools and single cell data objects (Seurat, SingleCellExperiment, etc...)

Toolkit for highly memory efficient analysis of single-cell RNA-Seq, scATAC-Seq and CITE-Seq data. Analyze atlas scale datasets with millions of cells on laptop.

Google News and Leo Tolstoy: Visualizing Word2Vec Word Embeddings using t-SNE.

ProtVec can be used in protein interaction predictions, structure prediction, and protein data visualization.

This repository contains R code, with which you can create 3D UMAP and tSNE plots of Seurat analyzed scRNAseq data

A Uniform Manifold Approximation and Projection (UMAP) library for Java, developed by Tag.bio in collaboration with Real Time Genomics.

Using machine learning on your anki collection to enhance the scheduling via semantic clustering and semantic similarity

Comparing FC VAE / FCN VAE / PCA / UMAP on MNIST / FMNIST

BEER: Batch EffEct Remover for single-cell data

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).

High performance t-SNE for Ruby

scGEAToolbox: Matlab toolbox for single-cell gene expression analyses

Scripts demo-ing how to train a Word2Vec model and reduce its vector space

Playing with MNIST. Machine Learning. Generative Models.

Deep Learning API and Server in C++14 support for Caffe, Caffe2, PyTorch,TensorRT, Dlib, NCNN, Tensorflow, XGBoost and TSNE

Parallel t-SNE implementation with Python and Torch wrappers.

Machine learning algorithm such as KNN,Naive Bayes,Logistic Regression,SVM,Decision Trees,Random Forest,k means and Truncated SVD on amazon fine food review

Uniform Manifold Approximation and Projection

Workshop (6 hours): Clustering (Hdbscan, LCA, Hopach), dimension reduction (UMAP, GLRM), and anomaly detection (isolation forests).

A fast, accurate, and modularized dimensionality reduction approach based on diffusion harmonics and graph layouts. Escalates to millions of samples on a personal laptop. Adds high-dimensional big data intrinsic structure to your clustering and data visualization workflow.

Uniform Manifold Approximation and Projection (UMAP) implementation in Julia