We just learned how to write and use a simple neural network to warn a user when they are getting too close to an object. As you look through the code, appreciate that most of these values are internally adjusted as part of training. When using a neural network, it is important to understand these basic principals:
- Activation function: If you are not using sigmoid, then you will also need to find the partial derivative of your activation function in order to use gradient descent with backward propagation.
- # Input neurons: This will not only set the complexity of the network, but it will also determine the number of hidden or middle layer of neurons.
- # Output neurons: How many outputs or ways do you need ...
