From Deep Learning Foundations to Stable Diffusion

About this course

This advanced course takes you beyond using deep learning frameworks — you'll build them from the ground up. Created by fast.ai, a respected leader in making AI education practical and accessible, Part 2 culminates in understanding and implementing Stable Diffusion, one of the most impactful generative AI models today. If you've grasped the fundamentals and want to understand *how* deep learning really works under the hood, this is your next step.

What you'll learn

• Write deep learning libraries and neural network architectures from scratch using PyTorch, moving beyond pre-built APIs
• Understand backpropagation, gradient descent, and the mathematical foundations that power modern AI models
• Build and train convolutional neural networks (CNNs) for computer vision tasks without relying on high-level wrappers
• Implement attention mechanisms and transformer architectures that form the backbone of today's large language models
• Reproduce and fine-tune Stable Diffusion, a state-of-the-art generative model, from first principles
• Debug and optimize deep learning code for real-world performance constraints
• Apply these skills to create your own AI applications, from image generation to custom model architectures

Who this is for

You're ready for this course if you've completed Part 1 or equivalent foundational deep learning knowledge. You're comfortable with Python, understand basic neural network concepts, and you're motivated by understanding *why* things work, not just *that* they work.

• AI researchers and graduate students — deepen your theoretical understanding and build publication-ready code from scratch
• ML engineers transitioning to advanced roles — master the internals you'll need for specialized positions in generative AI and research teams

Prerequisites

You should be comfortable with Python programming, familiar with basic neural networks and backpropagation from an introductory course, and have hands-on experience with at least one deep learning framework. Completion of fast.ai Part 1 or equivalent is strongly recommended.

Why this matters for Indian learners

India's AI sector is shifting from implementation roles toward research and model development. Companies like Google Research India, Microsoft Research India, and growing startups in Bangalore and Pune are hiring engineers who can build and customize deep learning systems. Generative AI skills specifically are creating high-demand roles in AI research labs and product teams, where salaries exceed those of conventional ML engineering positions. Mastering the foundations taught here prepares you for these premium opportunities.

Frequently asked questions

Is this course really free?

Yes, completely free. No hidden fees, no paywall for materials.

How long will it take to complete?

Plan for about 50 hours total. If you dedicate 5–7 hours per week, you could complete it in 7–10 weeks. Adjust based on your pace and how deeply you experiment with the code.

Will I get a certificate?

This course does not offer a formal certificate, but you'll have real code and working models to show as proof of learning — often more valuable in AI roles than a badge.