30-days-PyTorch
30-days PyTorch Practice from Beginning to Advance
APACHE-2.0 License
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30-days-PyTorch
| S.No. | Topic | Sub-Topic | Link |
|---|---|---|---|
| 0. Welcome and "What is deep learning?" | |||
| 1. Why use machine/deep learning? | |||
| 2. The number one rule of ML | |||
| 3. Machine learning vs deep learning | |||
| 4. Anatomy of neural networks | |||
| 5. Different learning paradigms | |||
| 6. What can deep learning be used for? | |||
| 7. What is/why PyTorch? | |||
| 8. What are tensors? | |||
| 9. Outline | |||
| 10. How to (and how not to) approach this course | |||
| 11. Important resources | |||
| 12. Getting setup | |||
| 13. Introduction to tensors | |||
| 1 | 🛠 Chapter 0 – PyTorch Fundamentals | 14. Creating tensors | |
| 17. Tensor datatypes | |||
| 18. Tensor attributes (information about tensors) | |||
| 19. Manipulating tensors | |||
| 20. Matrix multiplication | |||
| 21. Finding the min, max, mean & sum | |||
| 22. Reshaping, viewing and stacking | |||
| 23. Squeezing, unsqueezing and permuting | |||
| 24. Selecting data (indexing) | |||
| 25. PyTorch and NumPy | |||
| 26. Reproducibility | |||
| 27. Accessing a GPU | |||
| 28. Setting up device agnostic code |