Neural network implementation guidelines

Today to conclude my series on neural network I am going to write down some guidelines and methodology for developing, testing and debugging a neural network.

As we will see (or as you already experienced) implementing a neural network is tricky and there is often a thin line between failure and success – between something that works great and something making absurd predictions.

The number of parameters we need to adjust is just great: from choosing the right algorithm, to tuning the model hyper-parameters, to improving the data, ….

In fact we need a good methodology and a solid understanding of how our model works and what is the impact of each of its parameters.
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Recurrent Neural Network

After introducing the convolutional neural networks I continue my serie on neural networks with another kind of specialised network: the recurrent neural network.


The recurrent neural network is a kind of neural network that specialises in sequential input data.

With traditional neural network sequential data (e.g. time series) are split into fixed-sized windows and only the data points inside the window can influence the outcome at time t.

With recurrent neural network the network can remember data points much further in the past than a typical window size.
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