11-785: Introduction to Deep Learning
Carnegie Mellon's rigorous deep learning course with comprehensive homework assignments building models from basic math.
- Duration
- 60+ hours
- Price
- Free
- Format
- Video
- Level
- Advanced
Free
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What you'll learn
- Write a custom autograd and backpropagation engine from pure NumPy
- Implement sequence-to-sequence models with CTC decoding
- Analyze optimization dynamics: Adam, RMSprop, learning rate schedulers
- Train transformer language models on distributed GPU clusters
About this course
Taught by Bhiksha Raj and Rita Singh at CMU's Language Technologies Institute. Renowned for its challenging homeworks where students implement autograd engines, MLP classifiers, CNNs, and attention layers with zero high-level library abstractions before graduating to PyTorch.
Prerequisites
Strong linear algebra, calculus, and C++/Python systems programming