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 lesson teaches you how to obtain and analyse narrative texts for patterns of sentiment and emotion. The 'syuzhet' sentiment analysis algorithm, along with the programming language R, will be used, demonstrating the techniques to allow learners to follow along.
This lesson provides a beginner-friendly introduction to convolutional neural networks (CNNs) for image classification. The tutorial provides a conceptual understanding of how neural networks work by using Google’s Teachable Machine to train a model on paintings from the ArtUK database. This lesson also demonstrates how to use Javascript to embed the model in a live website.
In this lesson, you will use Qt Designer and Python to design and implement a simple graphical user interface and application to merge PDF files. This lesson also demonstrates how to package the application for distribution to other personal computers.
This lesson demonstrates how to build a basic interactive web application using Shiny, a library (a set of additional functions) for the programming language R. In the lesson, you will design and implement a simple application, consisting of a slider which allows a user to select a date range, which will then trigger some code in R, and display a set of corresponding points on an interactive map.
This lesson from Programming Historian introduces basic use of Map Warper for historical maps. It guides you from upload to export, demonstrating methods for georeferencing and producing visualizations.
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.
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.
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.
