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A recurrent neural network (RNN) is recurrent in the way it processes the sequences of data. This means that the same operation a is performed on all the data of the sequence input x, returning output ˆy of the specific unit for the specific point in the sequence t.

The figure below shows a simplified RNN, with one hidden layer, folded (left) and unfolded (right) through the dimension of time, where x_t is input for time t, U are the weights for input x to the hidden connections, W are the weights for output passed on in the hidden-to-hidden connection…

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Long Short-Term Memory (LSTM) is one of the most successful recurrent neural networks in modern real world applications because of its clever use of gates to keep or discard long and short-term information in its memory.

It was introduced in the Long short-term memory paper by Hochreiter & Schmidhuber (1997), and later on refined in the paper by Gers Felix et al. (2000) with the addition of a gate to the weights, making LSTM cells compatible with different sequence lengths. …

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Any neural network struggles with vanishing or exploding gradients when the computational graph becomes too deep. This happens with traditional neural networks when the number of layers is very large or to Recurrent Neural Networks (RNNs), like LSTM or GRU, when the sequence length of the input data is too big. The reason for the struggle of deep RNNs comes from the repeated multiplication of the parameters, especially weights when they are less or greater than one.

“Suppose that a computational graph contains a path that consists of repeatedly multiplying by a matrix W. After t steps, this is equivalent…

I have spent a lot of time during the last couple of weeks at work on sourcing and analyzing data to track business performance. However, when I reached the conclusion of my analysis, I realized that I had no way to present the analysis other than a csv-file as the output.

A csv-file is not a good tool of visualization, when you want to communicate important findings. An easy alternative would be to setup an Excel spreadsheet with some graphical visualizations communicating the findings of the analysis. …

This week at work, I automated the process of extracting historic data from thousands of excel files using Python. Here is how.

You might be in the same situation as me, where you need to get some data from a daily excel report stored in one or multiple folders on some drive. The report could be orders received, or whatever your colleagues or you have decided to save in excel day after day, year after year.

First of. This is important. The success of this task heavily depends on the current structure or lack of in the folder and naming…

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An programming amateur from Denmark, who tries to make his and your life easier with code.

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