Astronomy Image Colorization using Machine Learning (GANs)
What you'll learn
- Discover the fundamentals of Generative Adversarial Networks (GANs) and understand their architecture, loss functions, and optimization challenges.
- Generate galaxies using GANs by setting up and training a model from scratch with hands-on coding in Kaggle Notebooks.
- Dive deeper into Wasserstein GAN with Gradient Penalty (WGAN-GP), learning about the algorithm and its implementation for more stable training.
- Implement WGAN-GP to generate realistic galaxy images and compare generated images with real astronomical data.
- Master Image-to-Image Translation GANs (Pix2Pix) and explore how they can be used for transforming images in the context of astronomy.
- Colorize black-and-white astronomical images using UNET architecture, PyTorch, and advanced GAN models to recreate realistic, vivid space images.
- Get introduced to FastAPI and Streamlit, learn to build APIs and create a frontend for your machine learning models.
- Create and deploy your own Image Colorization App using FastAPI, bringing all your learning together in a real-world project.
Requirements
- Basic knowledge of Python programming.
- Familiarity with machine learning concepts is recommended, but not mandatory.
- Enthusiasm to learn GANs, WGANs, and image processing techniques!
Description
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.
Course Highlights:
Real-world Astronomy Applications: Work with real astronomical data to train your models.
Project-Based Learning: Build multiple projects, including a Galaxy Generation project and a colorization web app.
Hands-on with GANs: Deep dive into the technical details of GANs, WGANs, and Pix2Pix with step-by-step coding exercises.
PyTorch & FastAPI: Learn how to use PyTorch for model building and FastAPI to deploy your models in production.
Who This Course is For:
Data science enthusiasts interested in Generative Adversarial Networks (GANs).
Machine learning engineers looking to enhance their skills in computer vision and image generation.
Astronomy buffs who want to apply machine learning to space image processing.
Developers interested in building real-world ML apps using FastAPI and Streamlit.
Requirements:
Basic knowledge of Python programming.
Familiarity with machine learning concepts is recommended, but not mandatory.
Enthusiasm to learn GANs, WGANs, and image processing techniques!
FAQs Section:
What tools and libraries will we use in this course?
You'll use Python libraries like PyTorch for model building, FastAPI for backend development, and Streamlit for frontend interfaces. We'll also leverage Kaggle Notebooks for coding exercises.
Do I need prior experience with GANs?
No prior experience with GANs is necessary, but basic Python programming knowledge and a basic understanding of machine learning would be beneficial.
Who this course is for:
- Data science enthusiasts interested in Generative Adversarial Networks (GANs).
- Machine learning engineers looking to enhance their skills in computer vision and image generation.
- Astronomy buffs who want to apply machine learning to space image processing.
- Developers interested in building real-world ML apps using FastAPI and Streamlit.
Instructor
- 4.2 Instructor Rating
- 322 Reviews
- 5,907 Students
- 12 Courses
Spartificial is a platform for students of any domain to learn and work on projects that are solving urgent problems in real world. It is important for a student to learn with an intention for overall technical, social and mental development. We are fan of Project-based multi-domain learning because that's what life is and in our professional life, we are not judged by our test scores or grades but the quality of our work, and our knowledge of real-world.