Progressive Data Dropout or PDD is a set of techniques that help us intelligently pick the data points (and dropping the others) required for neural network training, to ensure the training process is efficient as well as better generalized. In our current framework, we provide 3 such approaches to drop data points iteratively across epochs, reducing the number of data points used progressively as we move forward with the training process.