cs-self-learning/docs/深度学习/CMU11-785.en.md

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# CMU 11-785: Introduction to Deep Learning
## Descriptions
- Offered by: CMU
- Prerequisites: Linear Algebra, Probability, Python Programming, and ML Foundations
- Programming Languages: Python
- Difficulty: 🌟🌟🌟🌟🌟
- Class Hour: ~120 hours
CMU 11-785 is a rigorous, fast-paced deep learning core course with very little filler. It starts from neural network fundamentals and systematically covers CNNs, RNNs, Attention/Transformers, optimization, and generalization.
The workload feels close to graduate-level training: assignments usually require real understanding of model behavior, training details, and experimental methodology. If you want durable deep learning fundamentals (instead of only using high-level APIs), this course is an excellent investment.
## Course Resources
- Course Website: <https://deeplearning.cs.cmu.edu/S26/index.html>
- Recordings: Lecture recordings are available on course websites (varies by semester)
- Textbooks: Mainly Lecture Notes / Slides + paper readings
- Assignments: Multiple programming assignments and a course project (published on course sites)
## Personal Resources
No public personal repository is currently provided for this course.