Gradient descent looks at the network as a calculus function and adjusts the values to minimize the loss function. Next, we will look at a variety of neural network styles that learn from and also ...

IDG. Figure 1. A diagram of the neural network we’ll use for our example. The idea in backpropagation with gradient descent is to consider the entire network as a multivariate function that ...

Because the log-sigmoid function constrains results to the range (0,1), the function is sometimes said to be a squashing function in neural network literature. It is the non-linear characteristics of ...