FAQ: Implementing Neural Networks - Neural network model: output layer

This community-built FAQ covers the “Neural network model: output layer” exercise from the lesson “Implementing Neural Networks”.

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This exercise can be found in the following Codecademy content:

Build Deep Learning Models with TensorFlow

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The output layer shape depends on your task. In the case of regression, we need one output for each sample. For example, if your data has 100 samples, you would expect your output to be a vector with 100 entries - a numerical prediction for each sample.

In our case, we are doing regression and wish to predict one number for each data point: the medical cost billed by health insurance indicated in the charges column in our data. Hence, our output layer has only one neuron.

So, I’m a bit confused here.
In the theoretical case, we are performing a regression with 100 samples. The output should be a vector with the following shape: [100,1].
In our case, we are also performing a regression with X samples, but we only want the medical cost. So we only want one neuron, shaped by the number of samples.
Is that it?