Machine Learning

Resources

• Automatic Text Recognition (ATR) - Step 2: Where and How to Get Images

  EN

  This tutorial explores where and how to find, create, and collect images of textual material, a crucial initial step in any process using Automatic Text Recognition (ATR).

• Automatic Text Recognition (ATR) - Step 1: Getting Started

  EN

  Kick off your journey into Automatic Text Recognition (ATR) with our introductory tutorial video. This is the first video of a tutorial series dedicated to extracting full text from scanned images.

• Interrogating a National Narrative with GPT-2

  EN

  In this lesson, you will learn how to apply a Generative Pre-trained Transformer language model to a large-scale corpus so that you can locate broad themes and trends within written text.

• The CLS INFRA Survey of Methods in Computational Literary Studies

  EN

  This resource from the CLS INFRA project offers an introduction to several research areas and issues that are prominent withinComputational Literary Studies (CLS), including authorship attribution, literary history, literary genre, gender in literature, and canonicity/prestige, as well as to several key methodological concerns that are of importance when performing research in CLS.

• Computer Vision for the Humanities: An Introduction to Deep Learning for Image Classification (Part 1)

  EN

  This is the first of a two-part lesson introducing deep learning based computer vision methods for humanities research. Using a dataset of historical newspaper advertisements and the fastai Python library, the lesson walks through the pipeline of training a computer vision model to perform image classification.

• Computer Vision for the Humanities: An Introduction to Deep Learning for Image Classification (Part 2)

  EN

  This is the second of a two-part lesson introducing deep learning based computer vision methods for humanities research. This lesson digs deeper into the details of training a deep learning based computer vision model. It covers some challenges one may face due to the training data used and the importance of choosing an appropriate metric for your model. It presents some methods for evaluating the performance of a model.

• Word Embeddings

  EN

  Natural language processing is one of the most powerful concepts in modern linguistics and computer science, bridging the understanding of language from human to machine, and in turn programming machines so they can perform complex linguistic tasks on their own. This short video introduces learners to the key concepts of word embeddings and how they can be used in digital humanities projects.

• What 300-Dimensional Fridges Can Tell Us about Language

  EN

  In this lecture from the Austrian Centre for Digital Humanities and Cultural Heritage (ACDH-CH), Dirk Hovy gives an introduction to the method called embeddings, and showcases several applications of it. Hovy shows how they capture regional variation at an intra- and interlingual level, how they distinguish varieties and linguistic resources, and how they allow for the assessment of changing societal norms and associations.