An intro to deep learning workshop run at CAA 2023 by the Netherlands eScience Center.

Workshop website: https://esciencecenter-digital-skills.github.io/intro-to-deep-learning-archaeology/

This is an hands-on introduction to the first steps in Deep Learning, intended for researchers who are familiar with (non-deep) Machine Learning.

The use of Deep Learning has seen a sharp increase of popularity and applicability over the last decade. While Deep Learning can be a useful tool for researchers from a wide range of domains, taking the first steps in the world of Deep Learning can be somewhat intimidating. This introduction aims to cover the basics of Deep Learning in a practical and hands-on manner, so that upon completion, you will be able to train your first neural network and understand what next steps to take to improve the model.

We start with explaining the basic concepts of neural networks, and then go through the different steps of a Deep Learning workflow. Learners will learn how to prepare data for deep learning, how to implement a basic Deep Learning model in Python with Keras, how to monitor and troubleshoot the training process and how to implement different layer types such as convolutional layers.

In April 2023 the Computer Applications and Quantitative Methods in Archaeology (CAA) Conference was held in Amsterdam. In the context of this conference the introduction to deeplearing workshop was held twice.

• 3rd of April 2023 as pre- conference workshop
• 13rd of April 2023 as part of the CAA Computational Archaeology Lab.

The workshop is based on this training material and has been adapted for and archaeological audience. The data that has been used for this workshop has been derived from the excavation activities that were performed between 2003 and 2012 for the construction of Rokin metrostation for the North/South metro line. These activties produced a datasets of more than 135.000 records of more than 700.000 artefacts at can be downloaded here. An interactive (and very fancy) website where a subset of the artefacts can be browsed through can be found here.