Denoising Autoencoders for Phenotype Stratification
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Updated
Nov 9, 2018 - HTML
Denoising Autoencoders for Phenotype Stratification
Abstract: The S&P500 is difficult to predict. Multi-factor models provide a useful framework for making returns predictions and for controlling portfolio risk. This paper explores a three-step process in predicting PCA and Autoencoders factors to generate multi-factor models from the S&P500 component securities.
Ipny and HTML files EDA Case Studies
code for Visualizing and Understanding the Relationship between PCA, Auto encoder and K-Means Clustering.
Its a image generation library which learns to generate patterns based on training data
A deep AutoEncoder model is used for credit card fraud detection, which includes a multi-layer network of encoders and decoders and implements the method of reconstructing data to find the error threshold and achieve classification of fraud cases
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