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Feedforward Neural Networks — Part 2
This article covers the content discussed in the Feedforward Neural Networks module of the Deep Learning course and all the images are taken from the same module.
In the previous article, we discussed the Data, Tasks, Model jars of ML with respect to Feed Forward Neural Networks, we looked at how to understand the dimensions of the different weight matrix, how to compute the output. In this article, we look at how to decide the output layer of the network, how we learn the weights of the network, how to evaluate the network.
How to decide the Output Layer?
And two main tasks that we will be dealing within most of the problems are Classification and Regression:
The above image represents the case for Multi-Class Classification. We are given an image as the input and we are passing it through all the layers and then finally predict output and we know that the true output, in this case, is a probability distribution where the entire mass is focussed on one outcome. The network will also produce a probability distribution, it will predict four values in the above case such that these 4 values sum up to 1 and each of the 4 values is greater than equal to 0.
The other problem we can look at is that we are given some features of a movie and we are interested in predicting multiple metrics for example for the above case we are interested in predicting IMDB rating, critic rating, RT rating, Box Office collection. As all of these are real numbers, this is a regression problem, and in this case, also, we are trying to regress 4 values at the output. And in general, we can say that we have ‘k’ outputs in the regression case where we are trying to regress ‘k’ values and the output in case of Classification problem could be ‘k’ values where we are predicting the probabilities corresponding to each of the ‘k’ classes.
Let’s see the Output Layer for the Classification problem:
