Creating Interactive Visualizations with Plotly
EN
This lesson demonstrates how to create interactive data visualizations in Python with Plotly’s open-source graphing libraries using materials from the Historical Violence Database.
Programming Historian
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.
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Transcribing Handwritten Text with Python and Microsoft Azure Computer Vision
ENTools for machine transcription of handwriting are practical and labour-saving if you need to analyse or present text in digital form. This lesson will explain how to write a Python program to transcribe handwritten documents using Microsoft’s Azure Cognitive Services, a commercially available service that has a cost-free option for low volumes of use.Clustering and Visualising Documents Using Word Embeddings
ENThis lesson uses word embeddings and clustering algorithms in Python to identify groups of similar documents in a corpus of approximately 9,000 academic abstracts. It will teach you the basics of dimensionality reduction for extracting structure from a large corpus and how to evaluate your results.Corpus Analysis with spaCy
ENThis lesson demonstrates how to use the Python library spaCy for analysis of large collections of texts. This lesson details the process of using spaCy to enrich a corpus via lemmatization, part-of-speech tagging, dependency parsing, and named entity recognition. Readers will learn how the linguistic annotations produced by spaCy can be analyzed to help researchers explore meaningful trends in language patterns across a set of texts.Sentiment Analysis with 'syuzhet' using R
ENThis 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.Creating Deep Convolutional Neural Networks for Image Classification
ENThis 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.Creating GUIs in Python for Digital Humanities Projects
ENIn 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.Making an Interactive Web Application with R and Shiny
ENThis 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.Scalable Reading of Structured Data
ENIn this lesson, you will be introduced to ‘scalable reading’ and how to apply this workflow to your analysis of structured data.Introduction to Map Warper
ENThis 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.Interrogating a National Narrative with GPT-2
ENIn 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.Computer Vision for the Humanities: An Introduction to Deep Learning for Image Classification (Part 1)
ENThis 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.