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Please read the slides in the supporting docs before trying out the Jupyter notebook included in the tool!
This tool contains a self-contained deep learning demo along with a dataset. The goal of the notebook is to walk users through a typical pre-processing, modeling, and prediction workflow for convolutional neural networks and variational autoencoders using materials & chemicals data.
NB: The dataset is a small solubility dataset, and is not meant to train accurate or state-of-the-art models. The focus of this demo is on walking a user through a deep learning workflow and API (Keras/Tensorflow), in the context of materials and chemicals data.
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