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anomaly-detection-using-unsupervised-learning

Outiers are rare but are very crucial. In this project, several methods to detect anomalies using Unsupervised Learning where no labelled dataset is given is presented. This work was done between August 2019- November 2019. This later on served as the base project for the Master Thesis which is available in other repository. Unfortunately, I am not open to share code for this one but for master thesis code is public. Hope it helps. As we are moving towards the Industry 4.0 era where Artificial Intelligence(AI) and the Internet of Things(IoT) are crucial and integral parts of the revolution. In this transition phase from manual to the automation of work using different machines, sensors are a very important component and they play a vital role in the setup. The connectivity and flow of data/ information between sensors and devices leads us to witness rapid growth of time-based data are known as time series. In this project we will be implementing the techniques and applications of machine learning and statistical analysis, getting familiar with pandas, matplotlib, NumPy and various other libraries using Python on available sensor data from industries and extract useful information and make it possible to detect outliers and perform conditional monitoring which in-turn will help in reducing cost, optimizing manual labour capacity, increase productivity, availability, reliability and keep downtime minimum. The main aim of the Research Project is to develop online multivariate analysis tool which fetches the data, impute the missing data, eliminates outliers and non- compliant data, perform unsupervised learning and inform the user in case of abnormality i.e., out of control situations.

awesome-design

🌟 Curated design resources from all over the world.

bookdown

Authoring Books and Technical Documents with R Markdown

dearpygui

Dear PyGui: A fast and powerful Graphical User Interface Toolkit for Python with minimal dependencies

drawio-desktop

Official electron build of draw.io

fit-sne

Fast Fourier Transform-accelerated Interpolation-based t-SNE (FIt-SNE)

flowchar

A tiny tool for conversion from pseudo-code to flowchart in character format.

ggally

R package that extends ggplot2

gradleuserguide

"Gradle User Guide" Chinese version

hair-style-recommendation-system

This is the research project that classifies the person's face and assigns the best fit hairstyle template onto the head of the person.

hair_style_recommendation

A recommendation system to identify the user’s face shape and recommends the most flattering hair style.

hair_style_recommendation1

hashing-on-nonlinear-manifolds

Matlab code for "Hashing on Nonlinear Manifolds"

image-tsne

example of t-SNE to fc7 activations of a convnet (keras + sklearn)

keras-tsne

keras-tsne-1

kotlindl

High-level Deep Learning Framework written in Kotlin and inspired by Keras

libre-logos

A collection of free logos for open source projects

logo-generator

A simple yet effective Logo Generator using Fonts, Text, Icons & Colours.

logo_detection

logo_detection-1

Logo detection and brand recognition

logo_maker

Open Source Logo Maker From Decabits Lab

logomaker

Online logo editor in HTML 5 Canvas

logomator

An online logo maker that uses machine learning to make you feel like you're working with a real designer.

logosearch

LogoSear.ch and IconSear.ch: search through zillions of SVG logos/icons directly linked to their source repos.

lvdmaaten.github.io

Website of Laurens van der Maaten

mnist-visualization-via-tsne

This repository contains a simple script built in order to visualize the MNIST dataset in a 2D space

mnist_tsne

t-sne visualization of mnist images when feature is represented by raw pixels and cnn learned feature

neuro-imaging-in-python

Nilearn is a Python module for fast and easy statistical learning on NeuroImaging data. It leverages the scikit-learn Python toolbox for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis.

nonlineardotplots

Implementation of the algorithm described in the paper "Nonlinear Dot Plots" by Nils Rodrigues and Daniel Weiskopf