Programming Historian offers novice-friendly, peer-reviewed lessons that help humanists learn a wide range of digital tools, techniques, and workflows to facilitate research and teaching.
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.
This lesson is the second in a two-part lesson focusing on regression analysis. It provides an overview of logistic regression, how to use Python (Scikit-learn) to make a logistic regression model, and a discussion of interpreting the results of such analysis.
This lesson is the first of a two-part lesson focusing on an indispensable set of data analysis methods, logistic and linear regression. It provides an overview of linear regression and walks through running both algorithms in Python (using Scikit-learn). The lesson also discusses interpreting the results of a regression model and some common pitfalls to avoid.
In this lesson from Programming Historian, you will learn how to display a georeferenced map from Map Warper in KnightLab's StoryMap JS, an interactive web-based map and storytelling platform.
Researchers often need to be able to search a corpus of texts for a defined list of terms and historians are often interested in certain places named in a text or texts. This lesson details how to programmatically search documents for a list of terms, including place names and then how to obtain coordinates and map historical place names with the World Historical Gazetteer.
