Hello @Thala,
I think we are discussing the following slide. My answer would be the output of the neuron in the output layer predicts the probability of being a top-seller. Nothing more, and nothing less.
First, generally speaking, we can’t interpret the outcome of just any neuron. So we can’t just give a human understandable name to say, “OK, this neuron means exactly this or that”.
Second, there is a time when we can interpret the outcome of a neuron to carry a certain property, and that is when we explicitly constrain the neuron to carry that property.
For the example in the slide, when we train that neural network, (1) we use sigmoid as the activation for the neuron in the output layer, AND we use the log loss function as the loss function; and (2) our label is whether the product is a top seller. (1) & (2) together gives the outcome of the neuron of the output layer a meaning of probability, and therefore, we can intrepret that single particular neuron as the “probability of being a top seller”.
However, we never constrain those intermediate (hidden) layers. Meaning that, we don’t have label data for affordability, and we never constrain any neuron’s output to be consistent with the label data for affordability. Without such constraint, we can’t interpret those hidden layers’ neurons as “affordability” or anything else. They are just neurons which are very helpful in providing inputs for the neuron in the output layer to predict the probability of being a top seller. - which is also, I believe, what Professor Andrew Ng was trying to demonstrate, but for the sake of understanding and discussion, perhaps giving the neurons of the hidden layer some names can be very helpful, and that’s why you see affordability, awareness and so. So naming hidden layers’ neurons is just for demonstrating the concept about neural network and we can’t easily do this in practice without (1) the constraint that I described in above that happens during the training process or (2) an in-depth analysis of the outcome of the neurons after the training process.
Cheers,
Raymond