Backpropagation

What Is Backpropagation

Backpropagation is a technique for machine learning that uses a backward pass to update the model's parameters. The goal of the algorithm is to reduce the mean squared error (MSE) as much as possible. The following actions are taken during backpropagation in a network with a single layer:Follow the path through the network from the input all the way to the output by computing the output of the hidden layers as well as the output layer. [This Is the Step of Feedforward]Calculate the derivative of the cost function with respect to the input layer and the hidden layers using the information available in the output layer.Repeatedly update the weights until they converge or sufficient iterations have been applied to the model, whichever comes first.

How You Will Benefit

(I) Insights, and validations about the following topics:

Chapter 1: Backpropagation

Chapter 2: Chain rule

Chapter 3: Perceptron

Chapter 4: Artificial neuron

Chapter 5: Total derivative

Chapter 6: Delta rule

Chapter 7: Feedforward neural network

Chapter 8: Multilayer perceptron

Chapter 9: Vanishing gradient problem

Chapter 10: Mathematics of artificial neural networks

(II) Answering the public top questions about backpropagation.

(III) Real world examples for the usage of backpropagation in many fields.

Who This Book Is For

Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of backpropagation.

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