Astronomy Image Colorization using Machine Learning (GANs)

Are you fascinated by the beauty of the universe but curious about how machine learning can be used to bring astronomical images to life. Welcome to Astronomy Image Colorization using Machine Learning (GANs), where you will dive deep into the world of Generative Adversarial Networks (GANs) and their applications in astronomical image processing. In this course, you will learn how to leverage machine learning techniques to generate galaxies and colorize black-and-white images from space.

You will gain practical knowledge by building end-to-end projects, from understanding GANs to creating your own image colorization app using FastAPI and Streamlit. What You'll Learn:Module 1: Discover the fundamentals of Generative Adversarial Networks (GANs) and understand their architecture, loss functions, and optimization challenges. Module 2: Generate galaxies using GANs by setting up and training a model from scratch with hands-on coding in Kaggle Notebooks.

Module 3: Dive deeper into Wasserstein GAN with Gradient Penalty (WGAN-GP), learning about the algorithm and its implementation for more stable training. Module 4: Implement WGAN-GP to generate realistic galaxy images and compare generated images with real astronomical data. Module 5: Master Image-to-Image Translation GANs (Pix2Pix) and explore how they can be used for transforming images in the context of astronomy.

Module 6: Colorize black-and-white astronomical images using UNET architecture, PyTorch, and advanced GAN models to recreate realistic, vivid space images. Module 7: Get introduced to FastAPI and Streamlit, learn to build APIs and create a frontend for your machine learning models. Module 8: Create and deploy your own Image Colorization App using FastAPI, bringing all your learning together in a real-world project.