What is the learning rule for a perceptron called?Select one:a.The Backpropagation Algorithmb.The Perceptron Learning Algorithmc.The Hebbian Ruled.The Delta Rule

What is the Hebb rule?Select one:a.A rule used to determine the input to a neural networkb.A rule used to determine the structure of a neural networkc.A rule used to calculate the output of a neural networkd.A rule used to adjust the weights in a neural network

What is a perceptron?Select one:a.A type of machine learning algorithm for classification tasksb.A type of artificial neuron that can be trained to recognize patternsc.A type of deep learning neural networkd.A type of unsupervised learning algorithm

In the weight update rule of the neural network, w  = w - a \delta w1 pointa is called learning ratea is called Hebbian constanta is called momentumNone of the above

What is perceptron model in artificial neural network? Draw its diagram.

PerceptronPerceptron is Machine Learning algorithm for supervised learning of various binary classificationtasks. Further, Perceptron is also understood as an Artificial Neuron or neural network unit that helpsto detect certain input data computations in business intelligence.Perceptron model is also treated as one of the best and simplest types of Artificial Neural networks.However, it is a supervised learning algorithm of binary classifiers. Hence, we can consider it as asingle-layer neural network with four main parameters, i.e., input values, weights and Bias, net sum,and an activation function. Input Nodes or Input Layer:This is the primary component of Perceptron which accepts the initial data into the system for furtherprocessing. Each input node contains a real numerical value. Wight and Bias:Weight parameter represents the strength of the connection between units. This is another mostimportant parameter of Perceptron components. Weight is directly proportional to the strength of theassociated input neuron in deciding the output. Further, Bias can be considered as the line of interceptin a linear equation. Activation Function:These are the final and important components that help to determine whether the neuron will fire ornot. Activation Function can be considered primarily as a step function.Types of Activation functions: Sign function Step function, and Sigmoid functionThe data scientist uses the activation function to take a subjective decision based on various problemstatements and forms the desired outputs. Activation function may differ (e.g., Sign, Step, and Sigmoid)in perceptron models by checking whether the learning process is slow or has vanishing or explodinggradients