Haylemicheal Berihun Mekonnen's Projects
Testing and measuring AdCampaign perfomance.
AirBnB_clone
End-to-End Web3 dApps: certificate generation, distribution, and value transfer with Algorand NFTs and smart contracts
All in one paper implementation
engineering devops
I'm now a ALX Student, this is my first repository as a full-stack engineer
Recently, realistic image generation using deep neural networks has become a hot topic in machine learning and computer vision. Such an image can be generated at pixel level by learning from a large collection of images. Learning to generate colorful cartoon images from black-and-white sketches is not only an interesting research problem, but also a useful application in digital entertainment. In this paper, we investigate the sketch-to-image synthesis problem by using conditional generative adversarial networks (cGAN). We propose a model called auto-painter which can automatically generate compatible colors given a sketch. Wasserstein distance is used in training cGAN to overcome model collapse and enable the model converged much better. The new model is not only capable of painting hand-draw sketch with compatible colors, but also allowing users to indicate preferred colors. Experimental results on different sketch datasets show that the auto-painter performs better than other existing image-to-image methods.
Delivery drivers location optimisation with Causal Inference
Project code for cd0354 Monolith to Microservices at Scale course taught by Justin Lee
content for Udacity's cloud developer nanodegree
KPI maximisation through image analysis
Data warehouse tech stack with Postgresql, DBT, and Airflow
Projects and exercises for the latest Deep Learning ND program https://www.udacity.com/course/deep-learning-nanodegree--nd101