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A sequence to sequence prediction for developing a classification system is of very much required in developing applications. Standard approaches for developing applications won't help in providing accuracy. Hence, as an example let's take an IMDB movie review dataset and create some benchmarks by using RNN, RNN with LSTM and drop out rate, RNN with CNN, and RNN with CNN plus drop out rate to make a composite sequence to sequence classification work. We can compare the model accuracy as well.
In this tensorlfow project, our goal is to correctly identify digits from a dataset of tens of thousands of handwritten images.
In this data science project, we are going to work on video recognization data and a robust level of image recognization MNIST data.
Deep Learning Project- Learn to apply deep learning paradigm to forecast univariate time series data.