Notes for Deep Learning Systems from CMU
- Lec2. ML Refresher / Softmax Regression
- Lec3. Manual Neural Networks / Backprop
- hw0
- Lec4. Automatic Differentiation
- Lec5. Automatic Differentiation Implementation
- Lec6. Optimization
- Lec7. Neural Network Library Abstractions
- Lec8. NN Library Implementation
- hw1
- Lec9. Normalization, Dropout, + Implementation
- Lec10. Convolutional Networks
- Lec11. Hardware Acceleration for Linear Algebra
- Lec12. Hardware Acceleration + GPUs
- Lec13. Hardware Acceleration Implementation
- Lec14. Convolutions Network Implementation
- Lec15. Sequence Modeling + RNNs
- Lec16. Sequence Modeling Implementation
- Lec17. Transformers and Autoregressive Models
- Lec18. Transformers Implementation
- Lec19. Training Large Models
- Lec20. Generative Modelså
- Lec21. Generative Models Implementation
- Lec22. Customize Pretrained Models
- Lec23. Model Deployment
- Lec24. Machine Learning Compilation and Deployment Implementation