Özlem Kayıkcı's starred repositories
awesome-motoko
A curated list of Motoko code and resources.
cleansheets
Spreadsheet-like application for the Internet Computer, written in Motoko.
dotnet-core-ile-web-programlama
ASP .NET Core ile Web Programlama Ders Notları
YazilimMulakatSorulari
Jr., Mid., Sn. Pozisyonlarında karşılaşabileceğiniz mülakat soruları ve cevapları.
Fabric-Defect-Detection-
Context In the context of textile fabric, rare anomaly can occurs, hence compromising the quality of the tissues. In order to avoid that in some scenario, it is crucial to detect the defect. This dataset is for educational purposes Content Image size: 32x32 or 64x64 classes: ['good', 'color', 'cut', 'hole', 'thread', 'metal contamination'] rotations: 8 different rotations in [0, 20, 40, 60, 80, 100, 120, 140] Given an image size, a train and test dataset are available with randomly generated patches. Source images from the train and test are non-overlapping different tasks are possible: classification of the classes type classification of angles using only "good" images and testing of other classes texture representation learning / self-supervised learning Acknowledgements Based on the public dataset by the MVTec company Paul Bergmann, Michael Fuser, David Sattlegger, Carsten Steger. MVTec AD - A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection; in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019 Inspiration the main goal of this dataset is to explore self-supervised learning on texture images in order to solve anomaly detection problems and learn a robust representation of texture in lieu of traditional image processing features (e.g. glcm, gabor,….)
Human-Activity-Recognition-with-Neural-Network-using-Gyroscopic-and-Accelerometer-variables
The VALIDATION ACCURACY is BEST on KAGGLE. Artificial Neural Network with a validation accuracy of 97.98 % and a precision of 95% was achieved from the data to learn (as a cellphone attached on the waist) to recognise the type of activity that the user is doing. The dataset's description goes like this: The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used.
awesome-python
An opinionated list of awesome Python frameworks, libraries, software and resources.
developer-roadmap
Interactive roadmaps, guides and other educational content to help developers grow in their careers.
Unique-Github-Profiles
List of GitHub profiles that have awesome customization that you can use for inspiration.
argon-dashboard-asp-net
Start your development with a Bootstrap 4 Admin Dashboard built for ASP.NET Core framework, the newest go-to technology from Microsoft for top companies.
My_Carbon_Footprint_Calculator
My Carbon Footprint Calculator is an interactive website intended to help average everyday households find ways to reduce their carbon emissions.
CarbonFootprintCalculator
Calculate your carbon footprint, and find out how much you have to do to eliminate it!
carbontracker
Track and predict the energy consumption and carbon footprint of training deep learning models.
carbon_footprint
👣 Calculate your carbon footprint easily using a command line interface (10+ metrics, .PDF report).
Web-Dev-For-Beginners
24 Lessons, 12 Weeks, Get Started as a Web Developer
Computer-organization
Verilog code for the class
E-Ticaret-Sitesi
Dinamik olarak Javascript ile çalışan bir e-ticaret projesi
udemy-uygulamali-modern-web-gelistirme-egitimi
Udemy üzerinde yayınladığım Uygulamalı Modern Web Geliştirme Eğitimime ait kodlar.
Php-ile-E-ticaret-sitesi
E ticaret sitesi
Ayakkabi-Dukkani-E-Ticaret
E-Ticaret Web Sitesi