A practical guide to topic mining and interactive visualizations

Deepfakes Video classification via CNN, LSTM, C3D and triplets [IWBF'20]

Use your classification neural network for object detection and localization

code to help with tsne plotting

This repository contains my solution to the Stanford Course cs224u "Natural Language Understanding" Summer 2019

Exploring the discriminating power of different loss functions for classification

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

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

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

NLP of Ted Talk transcripts and a recommender.

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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.

Implementation of Variational Auto Encoder (VAE) in pytorch using MNIST data

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.

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Here using various data visualisation techniques likes TSNE and PCA we will try to visualise various kinds of dataset which is one of the most important part of building a machine learning model

Code to detect credit card fraud detecton

This repository contains notebooks which explores the tsne algorithm by applying it on various datasets

Testing AutoNovel and UNO novel-class-discovery techniques to assess their prerformances under different settings. Plot of tSNE to evaluate clustering capabilities for known and new classes

MNIST Digit recognition using machine learning techniques

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HDP + T-SNE + k-NN applied to topic modeling

DRAW aims to extract effective inferences from online drug reviews that would benefit drug users, pharma companies, and clinicians by receiving feedback on the drug based on opinion mining.

Successfully performed unsupervised clustering on student social network profiles to identify behavior patterns and group similarities, aiding in personalized engagement and targeted academic interventions.

Welcome to the CIFAR-10 SVM Classifier repository. This project demonstrates the implementation of a Support Vector Machine (SVM) model to classify images from the CIFAR-10 dataset. The repository is designed to provide insights into machine learning workflows and best practices, catering to both research and production-ready implementations.

2D/3D t-SNE Model Analysis

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t-SNE plot view tab for Single Cell Expression Atlas experiment page