When working on a project, sometimes it is necessary to use different programming languages to accomplish a task. The Reticulate Package in R creates a R interface to Python, allowing for R and Python to be used smoothly with each other. This presentation will give a brief overview of the Reticulate Package. As an example of the use of this package, I will present a Shiny application that I have been developing, and the steps I took in order to get Python working within the app.
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If you have built a Shiny app, you have some familiarity with the concept of reactivity. For the next two graphics-groups, we’ll talk about two alternatives to Shiny which also use reactivity:
Dash, using Python Observable, using JavaScript As examples, we’ll talk about how you can build the “same” app using all three tools. For the first session, we’ll discuss Shiny and Dash.
Here’s a reference I’m working on: https://ijlyttle.
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Automatic image captioning is the process of generating a descriptive text description for an image. Image captioning is one of the few applications of deep neural networks where we work with image and text data simultaneously. This captioning model can be trained using standard backpropagation techniques such as Stochastic Gradient Descent (SGD). I trained this model on the MS-COCO dataset with real-world images of humans, animals, vehicles, etc., in various situations and surroundings.
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