Official Companion Code for Deep Learning Crash Course v1.0

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