Creating input (x) and target (y) for RNNs

When creating training data for RNN, the target label for a given input label is the input label itself but shifted by one position. Please refer to the diagram below.

charRNN@0.5x
Continue reading “Creating input (x) and target (y) for RNNs”

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Handling missing values in a Dataset before training

How to impute missing values in a dataset before feeding to a classifier is often a difficult decision. Imputing with a wrong value can significantly skew the data and result in wrong classifier. The ideal solution is to get a clean data set without any NULL values but then, we might have to throw out most data. There are no perfect workarounds as most classifiers are built based on the information from data and lack thereof results in the wrong classifier. Continue reading “Handling missing values in a Dataset before training”