Python Natural Language Processing

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 and calculate the error gradient. The process is the same as we have seen in the gradient descent section. This error gradient gives you an indication of how you can optimize the generated output. Error gradient flows back in the ANN and starts updating the weight so that we get ...