Perceptrons: Fundamentals and Applications for The Neural Building Block
About this ebook
The perceptron is a technique for supervised learning of binary classifiers that is used in the field of machine learning. A function known as a binary classifier is one that can determine whether or not an input, which is often portrayed by a vector of numbers, is a member of a particular category. It is a kind of linear classifier, which means that it is a classification method that forms its predictions on the basis of a linear predictor function by combining a set of weights with the feature vector. In other words, it creates its predictions based on a linear predictor function.
How You Will Benefit
(I) Insights, and validations about the following topics:
Chapter 1: Perceptron
Chapter 2: Supervised learning
Chapter 3: Support vector machine
Chapter 4: Linear classifier
Chapter 5: Pattern recognition
Chapter 6: Artificial neuron
Chapter 7: Hopfield network
Chapter 8: Backpropagation
Chapter 9: Feedforward neural network
Chapter 10: Multilayer perceptron
(II) Answering the public top questions about perceptrons.
(III) Real world examples for the usage of perceptrons 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 perceptrons.
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