ozlemkayikcii

ozlemkayikcii's Stars

• kamranahmedse/developer-roadmap

  Interactive roadmaps, guides and other educational content to help developers grow in their careers.

  Language:TypeScript302k6.8k2.5k39.5k

• vinta/awesome-python

  An opinionated list of awesome Python frameworks, libraries, software and resources.

  Language:Python228k6.1k025k

• microsoft/Web-Dev-For-Beginners

  24 Lessons, 12 Weeks, Get Started as a Web Developer

  Language:JavaScript83.8k2.7k29212.4k

• soumyajit4419/Portfolio

  My self coded personal website build with React.js

  Language:JavaScript4.8k26762.6k

• alikarakoc/YazilimMulakatSorulari

  Jr., Mid., Sn. Pozisyonlarında karşılaşabileceğiniz mülakat soruları ve cevapları.

  63449017

• lfwa/carbontracker

  Track and predict the energy consumption and carbon footprint of training deep learning models.

  Language:Python404126630

• creativetimofficial/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.

  Language:CSS2191712364

• nicknochnack/MediaPipePoseEstimation

  Language:Jupyter Notebook20144179

• ic123-xyz/awesome-motoko

  A curated list of Motoko code and resources.

  1029048

• deadskull7/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.

  Language:Jupyter Notebook852030

• MarosMacko/CarbonFootprintCalculator

  Calculate your carbon footprint, and find out how much you have to do to eliminate it!

  Language:HTML835133

• serhattsnmz/dotnet-core-ile-web-programlama

  ASP .NET Core ile Web Programlama Ders Notları

  Language:C#7912016

• protea-earth/carbon_footprint

  👣 Calculate your carbon footprint easily using a command line interface (10+ metrics, .PDF report).

  Language:HTML381012

• muratbaseren/udemy-uygulamali-modern-web-gelistirme-egitimi

  Udemy üzerinde yayınladığım Uygulamalı Modern Web Geliştirme Eğitimime ait kodlar.

  Language:HTML2312028

• EPFLiGHT/cumulator

  A tool to quantify and report the carbon footprint of machine learning computations and communication

  Language:Jupyter Notebook213175

• Jaidevstudio/Unique-Github-Profiles

  List of GitHub profiles that have awesome customization that you can use for inspiration.

  1822315

• matthewhammer/cleansheets

  Spreadsheet-like application for the Internet Computer, written in Motoko.

  Language:Modelica11200

• kemalduran/Ayakkabi-Dukkani-E-Ticaret

  E-Ticaret Web Sitesi

  Language:PHP92

• himanshusharma9034/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,….)

  Language:Jupyter Notebook7

• najahiiii/linux

  go away.

  Language:C3000

• sercikul/periodontal-diagnosis-tool

  Language:JavaScript31

• sfbinay/E-Ticaret-Sitesi

  Dinamik olarak Javascript ile çalışan bir e-ticaret projesi

  Language:JavaScript3

• mshachnai/Posit-mathlib

  Language:C++2

• aylinaslan/Php-ile-E-ticaret-sitesi

  E ticaret sitesi

  Language:JavaScript1

• Dylan-Liew/Cultisk-Server

  Language:Python1

• gaming-hacker/jburkardt_cpp_src

  Language:C++1200

• montiqum/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.

  Language:HTML1

• Wholanz/Computer-organization

  Verilog code for the class

  Language:C1

• WonChung/General-Game-Playing

  Language:Python11

• zhangrace/general-game-playing

  Language:Python1