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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.
What Is Artificial Intelligence Series
The artificial intelligence book series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field.
The artificial intelligence book series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.
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.
What Is Artificial Intelligence Series
The artificial intelligence book series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field.
The artificial intelligence book series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.
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