This is the first stable public release of the official companion repository for
Deep Learning Crash Course (No Starch Press, 2026).
The repository provides fully worked, hands-on, project-based implementations covering the complete modern deep learning pipeline, from foundational neural networks to state-of-the-art generative and graph-based models.
✨ Highlights
- 14 self-contained chapters, each aligned with a chapter of the book
- End-to-end examples covering:
- Dense neural networks (classification & regression)
- Convolutional neural networks, U-Nets, and autoencoders
- Self-supervised learning exploiting symmetries
- Recurrent neural networks, attention mechanisms, and transformers
- Generative models: GANs and diffusion models
- Graph neural networks for relational data
- Active learning and reinforcement learning
- Reservoir computing for chaotic systems
- Designed for accessibility, clarity, and extensibility
- Suitable for education, research, and practical applications
📁 Repository structure
Each chapter is organized in a dedicated folder and can be run independently, making the material easy to explore, adapt, and reuse.
📖 Book reference
Deep Learning Crash Course
Giovanni Volpe, Benjamin Midtvedt, Jesús Pineda, Henrik Klein Moberg,
Harshith Bachimanchi, Joana B. Pereira, Carlo Manzo
No Starch Press, San Francisco (CA), 2026
ISBN-13: 9781718503922
https://nostarch.com/deep-learning-crash-course
🔧 Notes
- This release reflects the stable companion codebase corresponding to the book
- Future releases may include bug fixes, framework compatibility updates, and minor refinements