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ai-for-medical-diagnosis-1

This repositary consists of all the solutions of the Quiz and Programming Assignments for the "AI for Medical Diagnosis" in Coursera by deeplearning.ai. Please use this only for reference Purpose

ai-for-medicine

This repository is a summary of "AI for Medicine" lectured by Deeplearning.ai, Coursera. (https://www.coursera.org/specializations/ai-for-medicine)

ai-for-medicine-specialization

AI for Medicine Specialization - Coursera offered by deeplearning.ai

andrew-ng-machine-learning-notes

The offical notes of Andrew Ng Machine Learning in Stanford University

artificial-intelligence-tutorials

awesome-automotive

A curated list of delightful and free automotive engineering resources, looking for contributors ❗

awesome-conformal-prediction

A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers, PhD and MSc theses, articles and open-source libraries.

awesome-image-inpainting

A curated list of image inpainting and video inpainting papers and resources

classix

Fast and explainable clustering in Python

computer-graphics-with-webgl---sample-projects

2D and 3D Transformations, Lighting, Texture Properties, and more...

conformal-prediction

Lightweight, useful implementation of conformal prediction on real data.

conformasight

coursera-machine-learning-stanford

Machine learning-Stanford University

covid-19-deep-learning-cnn-model

Here, I created my own deep learning(CNN) model for early detection of COVID-19 from chest x-ray images. If we were to answer the question that why we need a deep learning model for early detection of COVID-19 from chest x-ray images, we can say the followings, doctors have seen that even if the test kits desined for diagnosis results in negative, the real results are positive for some patients when they review the chest X-ray images. For now the public dataset contains less amount of data which you can see in the dataset2 folder. We get this dataset from open-source https://github.com/ieee8023/covid-chestxray-dataset, but for sure it is not enough to train a proper deep learning model. But just to show that how easy it is to create an AI for the early detection of these kind of viruses. Just keep in mind that this cannot be used for diagnosis without training many more images in high-resolution and professinal medical tests. There you go! Let's work together to fight against COVID-19. As a tool, I used Keras with Tensorflow background, and the model can be improved by addig more convolution and pooling layers, and increasing the number of feature detectors'. Don't forget to upvote. Best Regards.

covid-19_detection_desktop_application_software

It is a Python desktop application software which takes a chest x-ray image and then produces probabilities belong to each class with a Grad-CAM.

covid-19_in_italy_exploratory_data_analysis

This repository is created in order to analyze why COVID-19 spreading faster in North Italy when compared to other regions of Italy.

covid-chestxray-dataset

We are building an open database of COVID-19 cases with chest X-ray or CT images.

ctci-6th-edition

Cracking the Coding Interview 6th Ed. Solutions

data-joensuu-pics-for-cyclegan

data-science-internship-works-at-turksat

Aim of this internship is first to learn about data science, big data visualization, machine learning and deep learning, implement the following tools: anaconda, jupyter notebook, spider and the libraries following: numpy,pandas,matplotlib,seaborn,plotly,scikit-learn by describing the platforms used by data scientists like kaggle and finally integrating the machine learning into e-government platform.

datasciencecoursera

Data Science Repo and blog for John Hopkins Coursera Courses. Please let me know if you have any questions.

datasingletest

datastructures

Here,Linked lists,Stacks,Queues; AVL,Red-Black,Binary Search Trees;Multidimensional arrays and Matrix operations are included.And in homeworks sample apllications are included.

frabiayapicioglu.github.io

Personal website, portfolio, resume & projects.

ganexample

general-advanced-deep-learning-trainings

Contents, •Neural networks – Perceptron, Adaline, BP neural networks, unsupervised learning neural networks, RBF neural networks, etc. •Optimization methods – Genetic algorithms, swarm intelligence, etc. •Training deep neural networks – Parameter and structure tuning, etc. •Deep learning neural network models – Convolutional Neural Networks (CNN), autoencoders

generating-art-cities-from-real-deep-learning-cycle-gan-

Our aim was to implement transformation from the city images to Monet’s style images. This report is constructed with seven parts. Part one, Overview, shares the main overview of this project Pix2Art. The second and third parts, Introduction and Information About The CycleGAN, briefly explain the usage of a CycleGAN. The fourth part, Data Collection, and Preprocessing shows how to collect and proceed image data. The fifth part, Experiments and Outcomes, and Results describe the experiment performed in the Pix2Art project and its outcomes. The seventh part, Reference, shows what articles are cited in this report. The source images shown in this report are from images taken by our members and/or Berkeley’s website.

github-slideshow

A robot powered training repository :robot:

image_to_image_translation_cyclegan

infographic

Infographic