Give a list of 5 questions that can be answered with the following context: "Since logistic regression corresponds to a linear neural network with no activation function, it should be apparent the multi-class neural network allows us to extend logistic regression to the multi-class setting. One way to accomplish this is to simply replace f(x, w) in eq. (15.18) with a linear function; while this is certainly a valid way to proceed, there is one slightly annoying side-effect. Recall from section 15.3.1 that in the case where we applied neural networks to a two-class classification task, the neural network had a single output neuron. However, in the multi-class setting considered in section 15.3.2, the neural network had as many outputs C as there was classes. This means that this approach to multi-class regression would not directly generalize the binary classification case. To get around this, it is customary to implement linear multi-class classification using the (modified) softmax with C −1 inputs which we encountered in eq. (5.31)." Give two different versions of responses. Please separate the responses with 6 asterisk symbols: ******.
user