Implementation of ANN
In this section, we will implement our first ANN in Python using numpy as our dependency. During this implementation, you can relate how gradient descent, activation function, and loss function have been integrated into our code. Apart from this, we will see the concept of backpropagation.
We will see the implementation of a single-layer NN with backpropagation.
Single-layer NN with backpropagation
Here, we will see the concept of backpropagation first, then we will start coding and I will explain things as we code.
Backpropagation
In a single-layer neural network, we have input that we feed to the first layer. These layer connections have some weights. We use the input, weight, and bias and sum them. This sum passes through the activation function and generates the output. This is an important step; whatever output has been generated should be compared with the actual expected output. As per the error function, calculate the error. Now use the gradient of the error function...
