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Practical Deep Learning for Coders Part 2: Deep Learning Foundations to Stable Diffusion
by Jeremy Howard · fast.ai
4.8(1,800 reviews)
100K+ enrolled14 weeksUpdated 2024-01
What You'll Learn
Understand the fundamentals and key concepts of deep learning
Apply deep learning techniques to solve real-world problems
Understand the fundamentals and key concepts of stable diffusion
Apply stable diffusion techniques to solve real-world problems
Understand the fundamentals and key concepts of diffusion models
Apply diffusion models techniques to solve real-world problems
About This Course
Goes from foundations of neural networks to implementing stable diffusion from scratch, covering backprop, attention, and diffusion math.
Curriculum
Module 1: Deep learning & Stable diffusion6 lessons
- Introduction to deep learning
- Deep learning in Practice
- Hands-on Exercise: Deep learning
- Introduction to stable diffusion
- Stable diffusion in Practice
- Hands-on Exercise: Stable diffusion
Module 2: Diffusion models & Attention6 lessons
- Introduction to diffusion models
- Diffusion models in Practice
- Hands-on Exercise: Diffusion models
- Introduction to attention
- Attention in Practice
- Hands-on Exercise: Attention
Module 3: PyTorch3 lessons
- Introduction to PyTorch
- PyTorch in Practice
- Hands-on Exercise: PyTorch
Instructor
Jeremy Howard
Instructor at fast.ai
Pros & Cons
Pros
- Highly rated by students
- Completely free to access
- High-quality video lectures
- Taught by Jeremy Howard
Cons
- No certificate provided
- Requires significant prior knowledge
- Self-paced requires discipline
Free
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