GPU Accelerated t-SNE for CUDA with Python bindings

Pytorch implementation for t-SNE with cuda to accelerate

Object classification with CIFAR-10 using transfer learning

A practical guide to topic mining and interactive visualizations

A research paper recommender system that recommends similar research papers based on abstract text of the papers.

This Machine Learning project deals with Coupon Recommendations based on Revenue Uplift

Interactive tool for building correlation maps between governments worldwide.

This is Matlab script for plotting 2 Dimensional and 3 Dimensional t-Distributed Stochastic Neighbor Embedding (t-SNE).

Breast region segmentation with multiatlas deformable registration

R package for automatic hyper parameter tuning and ensembles with deep learning, gradient boosting machines, and random forests. Powered by h2o.

Interactive Machine Learning Examples

Fast Barnes-Hut t-SNE with Tensorflow integration

An image search implementation in python using tensorflow keras, scikit-learn, scipy and matplotlib.

Dimensionality Reduction technique in machine learning both theory and code in Python. Includes topics from PCA, LDA, Kernel PCA, Factor Analysis and t-SNE algorithm

This is an implementation of 3 dimensionality reduction techniques - PCA, SVD, and tSNE for visualization of high dimensional data in 2D and 3D.

MODE-TASK plugin for PyMOL

Maximizing Revenue with Individualized Coupon Optimization Using Tree-Based Models

No description provided.

Training Word Embeddings and using them to perform Sentiment Analysis with attention based LSTMs

I am on the Advisory Services Team of a financial consultancy. One of MY clients, a prominent investment bank, is interested in offering a new cryptocurrency investment portfolio for its customers. The company, however, is lost in the vast universe of cryptocurrencies. They’ve asked me to create a report that includes what cryptocurrencies are on the trading market and determine whether they can be grouped to create a classification system for this new investment.

kernalized t-Distributed Stochastic Neighbor Embedding (t-SNE)

Implementation of t-SNE and Barnes-Hut-SNE algorithm. Comparison of algorithm implementation with sklearn library implementation on sample databases.

[no longer supported] for a more powerful version of this that supports multiple channels and uses machine learning, look here ➡️

A simple Jupyter notebook to visualize data in latent space using dimensionality reduction techniques.

EComp: Evolutionary Compression of Neural Networks Using a Novel Similarity Objective

Analysing different dimensionality reduction techniques and svm

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Advanced ML Case Study where we use ML algorithms to detect malware from a given piece of software.

Research for Parametric T-SNE in high to low dimensional data stream, published in 2021 by Kalebe Rodrigues Szlachta and Andre de Macedo Wlodkovski, oriented by Jean Paul Barddal, Computer Science graduation from Pontifical Catholic University of Parana (PUCPR)

Unsupervised-ML-t-SNE-Data-Mining-Cancer. Import Libraries, Import Dataset, Convert data to array format, Separate array into input and output components, TSNE implementation, Cluster Visualization