Machine learning with PyTorch

Tensors, autograd and training loops, then classifiers, convolutions, attention and a tiny language model, trained on the device.

Progress and your code are kept in this browser only.

1. 1Tensors
2. 2Autograd
3. 3Gradient descent
4. 4Modules and optimizers
5. 5Your own module
6. 6Classification
7. 7Training, test and overfitting
8. 8Dropout and weight decay
9. 9Mini-batches
10. 10Your own Dataset
11. 11Batch normalisation
12. 12Learning-rate schedules
13. 13Gradient clipping
14. 14Checkpoints and early stopping
15. 15Convolutions
16. 16Recurrent networks
17. 17Attention
18. 18A tiny language model