COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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A neuron computes an activation function followed by a linear function (z = Wx + b)
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A neuron computes a linear function (z = Wx + b) followed by an activation function
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A neuron computes a function g that scales the input x linearly (Wx + b)
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A neuron computes the mean of all features before applying the output to an activation function
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Detailed explanation-1: -Neural computation is the information processing performed by networks of neurons. Neural computation is affiliated with the philosophical tradition known as Computational theory of mind, also referred to as computationalism, which advances the thesis that neural computation explains cognition.
Detailed explanation-2: -The linear activation function, also known as “no activation, ” or “identity function” (multiplied x1.0), is where the activation is proportional to the input. The function doesn’t do anything to the weighted sum of the input, it simply spits out the value it was given.
Detailed explanation-3: -Neural networks are computing systems with interconnected nodes that work much like neurons in the human brain. Using algorithms, they can recognize hidden patterns and correlations in raw data, cluster and classify it, and – over time – continuously learn and improve.
Detailed explanation-4: -ReLU (Rectified Linear Unit) Activation Function The ReLU is the most used activation function in the world right now. Since, it is used in almost all the convolutional neural networks or deep learning.