Dilay Fidan Erçelik (dilayercelik)

dilayercelik

Geek Repo

Company:UCL -> Imperial

Location:London

Home Page:https://dilayercelik.github.io/

Twitter:@dilay_ercelik

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Dilay Fidan Erçelik's repositories

Arvato-MLProject

Udacity Machine Learning Engineer Nanodegree Capstone on customer segmentation and acquisition with Arvato Bertelsmann Financial Solutions.

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AIPND-learning

Code and associated files created or filled in, used in my learning during the AI Programming with Python Udacity Nanodegree Program

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AIPND-Project-1

Project 1: Use a Pre-trained Image Classifier to Identify Dog Breeds - AI with Python Programming Nanodegree (Udacity)

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CompNeuro_Washington

This repository contains answers to the weekly quizzes of the MOOC "Computational Neuroscience" offered by The University of Washington (Coursera - 8 weeks).

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COVID-19-FRANCE

COVID-19 FRANCE -January/February/March/7th April

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CSE160-Data-Programming-UW

This repository contains my answers to the homework assignments of the Data Programming (CSE160) course provided at the University of Washington and freely available online.

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Data-Science-Career-Path-Codecademy

Data Science Career Path is an extensive course on Codecademy on data science. It includes 26 modules, for an average workload/engagement of 350 hours, covering a range of subjects, such as: SQL, Python, NumPy, SciPy, Pandas, matplotlib, machine learning (scikit-learn), along interactive lessons/coding sessions, coding projects and capstone projects.

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dilayercelik.github.io

Personal GitHub Page, forked from the amazing Barry Clark's Jekyll Repo! ✨ Webpage at https://dilayercelik.github.io/

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snn-sound-localization

Training spiking neural networks for sound localization

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DGL24-Group-Project

DGL24-Group-Project

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ExampleExps_jsPsych

Learning how to use the jsPsych library and JS for online behavioural experiments creation

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gauss-bino-distributions-ercelik

Python Package for Gaussian and Binomial Probability Distributions.

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Image-Classifier-Flowers

This repository contains the files I have submitted for the final project of the AI Programming with Python Nanodegree offered by the School of AI, Udacity.

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Intro-to-Philosophy

Notes for the 'Introduction to Philosophy' course offered by The University of Edinburgh through Coursera.

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JPMorgan-virtual-internship

This repository includes the data files, code and patch files related to the modules of the virtual experience in software engineering at JPMorgan Chase & Co (Available online at InsideSherpa.com)

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Learn-R-Codecademy-Course

“Learn R” is an online course accessible on the Codecademy website that displays the fundamental programming concepts in R. It includes interactive lessons, coding sessions, as well as freeform projects to practice the skills gained throughout the modules. It comprises of 10 modules, worth approximately 20 hours of engagement.

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MLND-learning

Code and associated files created or filled in, used in my learning during the Machine Learning Engineer Udacity Nanodegree Program

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neural-networks-tfw1

Python Package for Implementing Plain Neural Networks with Tensorflow version 1. This project is the first of the Machine Learning Engineer Nanodegree offered at Udacity, School of AI, and is part of the Software Engineering Fundamentals module.

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NeuroML-Tumor

Exploratory Project on Brain Tumor Detection from MRI image data, using Residual Neural Networks (ResNet50)

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Plagiarism-Detector

Code and files associated with the Project called 'Plagiarism Detector', part of the Machine Learning Engineer Nanodegree at Udacity, School of AI. This project is the third of this Nanodegree, and is part of the module 'Machine Learning, Case Studies'.

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rl-bee-multimodal-sensing

Neuromatch group project: Relevance of sensory modalities for spatial navigation in foraging behaviours of the bee

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Sentiment-Analysis-Project-AWS

Code and files associated with the Project called 'Deploying a Sentiment Analysis Model', part of the Machine Learning Engineer Nanodegree at Udacity. This project is the second of this Nanodegree, and is part of the module 'Machine Learning in Production'

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