Polish Open SourceProfile
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- Warsaw, Poland
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- Warszawa
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- 15
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Computing the gap statistics from Tibshirani et. al. for various clustering algorithms
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- 13
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- Python
This project aims to quantify how accurately Morbus Parkinson's can be classified by different types of deep learning architecture without preprocessing the original sensor data. For this purpose, four different architectures (LSTM, ResNet, a basic autoencoder and a ResNet autoencoder) were used to evaluate the accuracy. The data was collected from patients at the University Hospital of Erlangen. Different severity levels of Parkinson's were regarded as being deceased. In this regard, this project performed a binary classification task (healthy and deceased). It shows, that a ResNet autoencoder predicts Parkinson with 87% accuracy and can be used as a decision support system for doctors.
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Jobs have been scraped from linkedin to perform a data analysis to optimize the CV and cover letter
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Data engineering fundamentals: 10 chapters, 15 live simulators, no signup, runs in your browser
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- 3
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- 3
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- JavaScript
IT-Based textgeneration with the use of NLP methods. A text summarization task is conducted with the amazon fine food review dataset from Kaggle. This task is done by attention and lstm neural networks.
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- TeX