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Computer Vision and Generative AI

Course

Learn how computers process and understand image data, then harness the power of the latest Generative AI models to create new images.

Learn how computers process and understand image data, then harness the power of the latest Generative AI models to create new images.

Intermediate

4 weeks

Real-world Projects

Completion Certificate

Last Updated January 30, 2024

Skills you'll learn:
Image pre-processing • Transfer learning • Word embeddings • Ethical AI
Prerequisites:
Transformer neural networks • Hugging Face • Deep learning

Course Lessons

Lesson 1

Introduction to Image Generation

In this lesson, you will define image generation and understand its relevance in AI and machine learning.

Lesson 2

Computer Vision Fundamentals

Learn how computers see images and perform key image processing techniques using classic image processing techniques such as image transformation, noise reduction, and more.

Lesson 3

Image Generation and GANs

Explore the landscape of Gen AI tools for Computer Vision and learn how they are evaluated. Learn what a generative adversarial network is and how it is utilized to generate images.

Lesson 4

Transformer-Based Computer Vision Models

In this lesson, we will be exploring Vision Transformers and the architecture that makes them work. Along the way we will explore Vision Transformers like DALL-E, DINO, and SAM.

Lesson 5

Diffusion Models

Learn the fundamentals of transformers. Then, get hands-on with the creation of a diffusion algorithm and work with Huggingface Diffusers to generate and work with images.

Lesson 6 • Project

AI Photo Editing with Inpainting

In this project, you will utilize Generative AI to take a famous painting and swap out the background with an image generated by Stable Diffusion.

Taught By The Best

Photo of Giacomo Vianello

Giacomo Vianello

Principal Data Scientist

Giacomo Vianello is an end-to-end data scientist with a passion for state-of-the-art but practical technical solutions. He is Principal Data Scientist at Cape Analytics, where he develops AI systems to extract intelligence from geospatial imagery bringing, cutting-edge AI solutions to the insurance and real estate industries.

Photo of Chuyi Shang

Chuyi Shang

UC Berkeley ML Researcher

Chuyi Shang is a machine learning researcher at Berkeley and a member of the Machine Learning @ Berkeley organization. He conducts research in video understanding and multimodal learning at Berkeley's AI Research Lab (BAIR), and has also conducted ML research at Berkeley's Haas School of Business.

Photo of Annabel Ng

Annabel Ng

UC Berkeley ML Researcher

Annabel Ng is an EECS undergrad at UC Berkeley, where she's researching brain-inspired vision models to improve image encodings in a Berkeley AI Research lab. She also leads the workshop division at Machine Learning @ Berkeley where she delivers lectures and develops interactive ML content for students.

Photo of Derek Xu

Derek Xu

UC Berkeley ML Researcher

Derek Xu is an ML Student Researcher at Sky Computing Lab. He's also an ML Engineer and Lecturer at ML@B and teaches a modern computer vision course at UC Berkeley. He is a 3rd year student at UCB studying EECS and Business Administration through the M.E.T. Program. In the past, he was also a SWE Intern at Salesforce.

Photo of Nathaniel Haynam

Nathaniel Haynam

ML Researcher at BAIR

Nathaniel Haynam is an ML Researcher at BAIR, where they push the edge of inverse reinforcement learning for multi-agent simulations. They are a ML Engineer and Lecturer in Machine Learning at Berkeley, teaching a modern computer vision course at UC Berkeley. They are a computer science major at UC Berkeley.

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Demonstrate proficiency with practical projects

Projects are based on real-world scenarios and challenges, allowing you to apply the skills you learn to practical situations, while giving you real hands-on experience.

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Reviewers provide timely and constructive feedback on your project submissions, highlighting areas of improvement and offering practical tips to enhance your work.

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