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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