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Andrew Marmon's Projects

covid-autoencoder-cv icon covid-autoencoder-cv

We propose an unsupervised learning approach that can be tied back to existing metadata, like mortality, age, BMI, etc. To accomplish this, we will train an Autoencoder model to create a low-dimensional representation of each image (Bank et al. 2020), and then use different clustering methods to determine optimal groupings for these images based on their encoding (Song et al. 2013)(Guo et al. 2017). Once these groups are instantiated, we can then associate image metadata to each cluster to determine whether there are statistically significant attributes tied to specific clusters. If it could be proven that attributes like mortality rate or success with intubation are linked to certain clusters, that information could be incredibly valuable for clinical outcomes. Also, although we have limited prognosis labels, we will also determine autoencoder performance by trying to classify the image based on the encoding using fully connected layers.

ohlc-data icon ohlc-data

📈 Scripts for fetching OHLC (incl. volume) data for cryptocurrencies from online exchanges, such as GDAX or Kraken.

resnet18-tensorflow icon resnet18-tensorflow

Implementation of ResNet18 using TensorFlow 2.0 Keras API. Currently ResNet 18 is not currently supported in base Tensorflow (see https://www.tensorflow.org/api_docs/python/tf/keras/applications for supported models), so a custom model is necessary to use this architecture.

show-recommender icon show-recommender

Recommend users TV shows using Neural Collaborative Filtering (https://www.comp.nus.edu.sg/~xiangnan/papers/ncf.pdf) in PyTorch. This repository contains all code for modelling, training, and predicting on incoming data, and provides a service to run the model to accept http requests and get top picks for the user.

united-nations-ner icon united-nations-ner

Fine-tuning a Hugging Face BERT model for the United Nations Named Entity Recognition task.

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