Pytorch implementation for t-SNE with cuda to accelerate
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Updated
Mar 29, 2023 - Python
Pytorch implementation for t-SNE with cuda to accelerate
Tutorial to make a simple NLP chatbot with Intent classification, FastText, Flask, AJAX
Учебные материалы по курсам связанным с Машинным обучением, которые я читаю в УрФУ. Презентации, блокноты ipynb, ссылки
Visualization of electron microscopy datasets with deep learning
This repo contains implementation of IP2Vec model which is used for learning similarities between IP Addresses
Robust way to explore GAN model latent space on web using three.js & t-SNE
Understand and Run Naive Bayes Algorithm on Dry Beans dataset
A python based tool for clustering text , CSV and logs.
Word Embedding visualization with T-SNE (t-distributed stochastic neighbor embedding) for BERT, ALBERT, ELMO, ELECTRA, XLNET, GLOVE.
Our first participation in a Kaggle competition. Dry Beans Classification is an unranked competition held by ITI AI-Pro.
Dashboard of UCSB ERI research outputs for Patterns 2021 paper
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 o…
Focused on advancing credit card fraud detection, this project employs machine learning algorithms, including neural networks and decision trees, to enhance fraud prevention in the banking sector. It serves as the final project for a Data Science course at the University of Ottawa in 2023.
Text analysis of scripts from the recent reboot of She-Ra and the Princesses of Power
Analysing different dimensionality reduction techniques and svm
3D T-SNE graphs with sliders and checkboxes to visualize the T-SNE cloud at every epoch for specific labels. Optionally you can also track specific datapoint by labeling it with a unique marker.
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
Implementation of t-SNE and Barnes-Hut-SNE algorithm. Comparison of algorithm implementation with sklearn library implementation on sample databases.
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