Neural Networks have a lot of knobs and buttons you have to set correctly to get the best possible performance out of it. Although some of them are self-explanatory and easy to understand (like the number of neurons in the input layer) and choose, there are many hyperparameters that are a bit more complex in terms of how they affect the outcome of the model (e.g. number of layers, the batch size or weight initialization).
In this lesson, we will look into the number of neurons in the input, output, and hidden layers and the number of hidden layers. We will learn the rules and best practices for determining the number of hidden layers and the number of neurons in there.
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6 окт 2024
