Neural Networks – Feedforward Math

In this post, we will do the math on our dummy dataset and calculate the feedforward steps by hand. We will take the parameters of our first instance, i.e. first house, as the input vector and arbitrarily chosen random weights. Our dummy dataset was as follows: INSTANCE SQFT NUM_BED NUM_BATH house 1 1500 2 2 house 2 1700 3 2 house 3 1750 3 3 dummy housing data Let’s recall how the feature vector and weights are multiplied to get the input to hidden layer which will be the values of our hidden nodes. Let’s see what goes into the … Continue reading Neural Networks – Feedforward Math