APPLICATION OF SUPERVISED LEARNING
NEURAL NETWORK
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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perceptron.fit(X ____ train)
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perceptron.fit(X ____ train, y ____ train)
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perceptron.predict(X ____ test)
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perceptron.predict(X ____ test, y ____ test)
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Detailed explanation-1: -Training a perceptron is an optimization problem which involves iteratively updating the weights in a way that minimizes the error function. We derived the error function and defined what an updated weight should be based on a current weight and the error calculated at the current iteration.
Detailed explanation-2: -The Perceptron is a linear machine learning algorithm for binary classification tasks. It may be considered one of the first and one of the simplest types of artificial neural networks. It is definitely not “deep” learning but is an important building block.
Detailed explanation-3: -Perceptron was introduced by Frank Rosenblatt in 1957. He proposed a Perceptron learning rule based on the original MCP neuron. A Perceptron is an algorithm for supervised learning of binary classifiers. This algorithm enables neurons to learn and processes elements in the training set one at a time.