Labsheets for the Applied Deep Learning course.
| Labsheet | Description |
|---|---|
| 0 | Introduction to Python and the scientific Python ecosystem |
| 1 | Your First Fully Connected Network |
| 2 | BC4 and Your First CNN |
| 3 | Techniques for Training DNNs |
| 4 | Data Augmentation |
If you have trouble viewing the labsheets on github, you can try using the NBViewer service provided by ipython.org.
In these labs we'll be using two computing environments:
- Colaboratory (a hosted version of Jupyter notebooks) for exploring PyTorch and dabbling with simple and non-computationally expensive experiments.
- Blue Crystal 4 (docs) for GPU accelerated experiments.
If instead you'd like to install Jupyter locally on your laptop, we provide some guidance on a best efforts basis. If you have trouble setting things up then we'd recommend using Colaboratory instead.
Kindly file an issue with a description of the problem you're facing, your setup, what you are observing and what you expect to happen instead.