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.
(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of backpropagation' technologies.
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.
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.
(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of backpropagation' technologies.
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.
Backpropagation: Fundamentals and Applications for Preparing Data for Training in Deep Learning
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Backpropagation: Fundamentals and Applications for Preparing Data for Training in Deep Learning
127
Product Details
| BN ID: | 2940167602175 |
|---|---|
| Publisher: | One Billion Knowledgeable |
| Publication date: | 06/21/2023 |
| Series: | Artificial Intelligence , #14 |
| Sold by: | PUBLISHDRIVE KFT |
| Format: | eBook |
| Pages: | 127 |
| File size: | 3 MB |