666 Open source projects that are alternatives of or similar to acoustic-keylogger

How to do Unsupervised Clustering with Keras

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Parallel & lightning fast implementation of available classic and contemporary variants of the KMeans clustering algorithm

Simple C++ Keylogger...

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Official adversarial mixup resynthesis repository

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A python-based time series aggregation module (tsam) which can be used to reduce the number of time steps using typical periods or by decreasing the temporal resolution

Awesome-Cross-Domain-Person-Re-identification

A keylogger for Windows, developed as part of the "Build an Advanced Keylogger using C++ for Ethical Hacking!" course on Udemy

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Pytorch Deep Clustering with Convolutional Autoencoders implementation

Official implementation of the CVPR 2022 paper "DETReg: Unsupervised Pretraining with Region Priors for Object Detection".

ST-DBSCAN: Simple and effective tool for spatial-temporal clustering

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An implementation of a Quartz Scheduler JobStore using Hazelcast distributed Collections

Golang Utilities for Data Analysis

자연어 처리와 텍스트 분석을 위한 오픈소스 파이썬 라이브러리 입니다.

Beta Machine Learning Toolkit

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Topic clustering library built on Transformer embeddings and cosine similarity metrics.Compatible with all BERT base transformers from huggingface.

[ICCV2021] Learning to Track Objects from Unlabeled Videos

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Image Denoising Codes using STROLLR learning, the Matlab implementation of the paper in ICASSP2017

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OpenMMLab Self-Supervised Learning Toolbox and Benchmark

Dual CNN Models for Unsupervised Monocular Depth Estimation

Flexible Statistics and Data Analysis (FSDA) extends MATLAB for a robust analysis of data sets affected by different sources of heterogeneity. It is open source software licensed under the European Union Public Licence (EUPL). FSDA is a joint project by the University of Parma and the Joint Research Centre of the European Commission.

Unsupervised learning of action classes with continuous temporal embedding (CVPR'19)

Awesome Graph Self-Supervised Learning

This repository is a recommended track, designed to get started with Machine Learning.

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