Pinned Repositories
AI_C
Buffet-Reservation-System
CityHack2019_submissions
Contestants are expected to upload the PowerPoint, code and other documents to this repository.
CityHack21
cityhack21-1
CW3
a prototype of cake-shop
dashboard
rechunter dashboard
DeepFake-Detector
Introduction Provided with a 12000-image dataset, our goal is to create a binary classifier capable of recognizing whether a given image is real or fake. We have used the following terms to refer to some important concepts throughout the paper: central face features - features that define the facial expression (eyes, mouth, nose, eyebrows). initial person/image - the person/image that was used as a base for the deepfake, for which the features were altered. donor person/image - the person/image from which the newly added/replaced features were taken. There are 3 classes of images, 4000 images each: real - images that were originally taken with a camera, not altered in any way. fake_face2face - images for which the topological shapes of central face features were altered, but their look (colors) was preserved, as if the initial person changed the facial expression but remained the same. fake_deepfake - images for which the entirety of the central face features (with colors) was transferred, as if the entirety of the initial person's face was swapped with the donor's face.
Lions
PoetBot
Using Emotion Classifier to classify poems into 6 categories and build a Chatbot with DialogFlow
iliasbatyrbekov's Repositories
iliasbatyrbekov/Lions
iliasbatyrbekov/AI_C
iliasbatyrbekov/Buffet-Reservation-System
iliasbatyrbekov/CityHack2019_submissions
Contestants are expected to upload the PowerPoint, code and other documents to this repository.
iliasbatyrbekov/CityHack21
iliasbatyrbekov/cityhack21-1
iliasbatyrbekov/CW3
a prototype of cake-shop
iliasbatyrbekov/dashboard
rechunter dashboard
iliasbatyrbekov/DeepFake-Detector
Introduction Provided with a 12000-image dataset, our goal is to create a binary classifier capable of recognizing whether a given image is real or fake. We have used the following terms to refer to some important concepts throughout the paper: central face features - features that define the facial expression (eyes, mouth, nose, eyebrows). initial person/image - the person/image that was used as a base for the deepfake, for which the features were altered. donor person/image - the person/image from which the newly added/replaced features were taken. There are 3 classes of images, 4000 images each: real - images that were originally taken with a camera, not altered in any way. fake_face2face - images for which the topological shapes of central face features were altered, but their look (colors) was preserved, as if the initial person changed the facial expression but remained the same. fake_deepfake - images for which the entirety of the central face features (with colors) was transferred, as if the entirety of the initial person's face was swapped with the donor's face.
iliasbatyrbekov/Fast-Approximate-Energy-Minimization-via-Graph-Cuts
Minimize Energy in Images.
iliasbatyrbekov/gameProj
iliasbatyrbekov/Instruments_dataset
iliasbatyrbekov/recHunter
iliasbatyrbekov/woocommerce
An open source eCommerce plugin for WordPress.