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torch-fsdd's Introduction

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Hi! My name is Ed, and I'm currently a machine learning engineer at Hazy working on improving a privacy-preserving synthetic data generation platform for enterprise data analytics.

I studied at the University of Edinburgh, with an MSc in Statistics with Data Science at the School of Mathematics, and a BSc in Computer Science at the School of Informatics.

I normally work with Python, mainly using it for machine learning or data related things, as well as any general purpose scripting, task automation or web development.

I did my fair share of Ruby development back in the day, and I am also familiar with the use of R for applied statistics, but lean heavily towards Python.

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torch-fsdd's Issues

Make it possible to apply separate transformations to data splits

Currently when we do the following, the same transformations are applied to the training, validation and test set.

transforms = Compose([...])
fsdd = TorchFSDDGenerator(version='master', transforms=transforms)
train_set, val_set, test_set = fsdd.train_val_test_split(test_size=0.15, val_size=0.15)

This obviously isn't ideal as there are some transformations (such as data augmentations) that you might want to apply to different splits.

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