Deep Learning for the Humanities Workshop
Deep Learning for the Humanities workshop
Deep-learning has been used in many recent applications You’ve likely seen use cases such as Stable diffusion - image generation, ChatGPT text generation, image classification and, object detection in self-driving cars. Deep-learning is also gaining popularity in the humanities, even beyond NLP uses.
In this workshop we are teaching humanities students & researchers how to train neural networks using PyTorch, thereby allowing them to eventually use this for their own research interests. Meaning this course is heavily focused onto the practical side of neural network programming.
The main focus revolves around visual data, but the basics taught in this course are equally important for language or audio related deep learning:
| Session | Date | Topic |
|---|---|---|
| Session 1 | 6 February | Tensors |
| Session 2 | 20 February | Linear regression (Training Pt 1.) |
| Session 2 | 5 March | Non-Linear Data (Training Pt 2. & Evaluation) |
| Session 3 | 19 March | Images as Data |
| Session 4 | 2 April | Image classification |
| Session 5 | 16 April | Transfer Learning |
| Session 6 | 30 April | Spill-over Session |
Location
Digital Lab, P.J. Veth 1.07 Time: biweekly starting 6th of Feb. 13-15