#devtools::install_github("rstudio/keras")įashion_mnist <- keras::dataset_fashion_mnist() The keras package contains the Fashion MNIST data, so we can easily import the data into RStudio from this package directly after installing it from Github and loading it. To start, we first set our seed to make sure the results are reproducible. As Python cannot be run in this blog post, I will walk you through the results from this script produced earlier, but if you would also like to see how to embed Python code and results in R Markdown files, check out this Markdown file on my Github! The R code used for this blog is also included on my Github. In the second blog post, we will experiment with tree-based methods (single tree, random forests and boosting) and support vector machines to see whether we can beat the neural network in terms of performance. To show you how to use one of RStudio’s incredible features to run Python from RStudio, I build my neural network in Python using the code in this Python script or this Jupyter notebook on my Github. I will also show you how to predict the clothing categories of the Fashion MNIST data using my go-to model: an artificial neural network. In this first blog of the series, we will explore and prepare the data for analysis. In this series of blog posts, I will compare different machine and deep learning methods to predict clothing categories from images using the Fashion MNIST data. She applies her interdisciplinary knowledge to computationally address societal problems of inequality. With a passion for data science and a background in mathematics and econometrics. Florianne Verkroost is a PhD candidate at Nuffield College at the University of Oxford.
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