![]() ![]() I also recommend Jon Duckett’s book JavaScript and jQuery: Interactive Front End Development 1. This library takes its inspiration from Processing and p5.js created by The Processing Foundation whose goal is “to promote software literacy within the visual arts, and visual literacy within technology-related fields - and to make these fields accessible to diverse communities.” For those needing a JavaScript refresher, FreeCodeCamp has excellent and free interactive tutorials. We will be using the ml5.js JavaScript library built on top of Tensorflow.js. To follow along with this section, you will need to have some familiarity with coding JavaScript. The latter half of the tutorial will take the neural network we train in Teachable Machine and embed it into a live website. While the default model we create in Teachable Machine will be biased towards our training data, it will suffice for pedagogical purposes and make apparent machine learning’s limitations. Teachable Machine contains a drag and drop interface that permits even those without coding experience to train a model. We will be using Google’s Teachable Machine to train our model - don’t worry if you don’t know what “training” a model is right now. Nonetheless, because of the issue’s complexity, this tutorial provides more background information than other tutorials focused on advanced coding. Although this removes some nuance, it also allows us to more easily gain an understanding of the general concept and how neural networks operate. Neural networks are a fascinating topic, and I have done my best to simplify my explaination of them. It is aimed at those wanting to gain an understanding of the basic terminology and makeup of neural networks so that they can expand their knowledge later, rather than those seeking to create production-level models from the outset. In contrast to other resources that focus on developing the most accurate model, the goal of this lesson is more modest. As historians, we can use these models to analyze which topics recur most often over time, or automate the creation of metadata for a database. In this tutorial, we will train a convolutional neural network to classify paintings. Understanding how these networks operate provides us with a way to explore their limitations when programmed to identify images they have not been trained to recognize. Neural networks develop their own idiosyncratic ways of seeing and often fail to separate features in the ways we might expect. This lesson provides a beginner-friendly introduction to convolutional neural networks, which along with transformers, are frequently-used machine learning models for image classification. Yet, the high level of statistics and computer science knowledge required to comprehend machine learning algorithms has resulted in critical analysis often failing to look inside the ‘black box’. Humanities scholars with expertise in issues of identity and power can serve as important bulwarks against growing digital inequality. Bias is rampant, and many machine learning techniques disproportionately damage women and communities of color. These innovations have enabled scholars to conduct large-scale explorations of cultural datasets previously requiring manual interpretation, but also bring their own set of challenges. In the last few years, machine learning has transformed computer vision and impacted a myriad of industries and disciplines. Transfer Learning and Convolutional Neural Networks.
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