Q.No 6. In machine learning, what is the primary task of an artificial neuron?a. Feature Extractionb. Activation Functionc. Weighted Sumd. All of the above
Question
Q.No 6. In machine learning, what is the primary task of an artificial neuron?a. Feature Extractionb. Activation Functionc. Weighted Sumd. All of the above
Solution
d. All of the above
Explanation:
In machine learning, an artificial neuron performs all of the tasks mentioned above.
a. Feature Extraction: It is the process of defining the inputs or variables that the model will use to make predictions.
b. Activation Function: It is used to introduce non-linearity into the output of a neuron. This is important because most real world data is non linear and we want neurons to learn these non linear representations.
c. Weighted Sum: Each input to a neuron has an associated weight which is learned during the training process. The neuron computes the weighted sum of its input which is then passed through an activation function to produce the output.
Similar Questions
Q.No.2. The primary task of an artificial neuron is to apply an ______________ to the weightedsum of inputs.
A neuron in an artificial neural network performs a ______________ operation followed by an activation function to produce an output.
Q.No 3. Which type of learning mimics the way the human brain processes information bycreating artificial neural networks?a. Supervised Learningb. Unsupervised Learningc. Reinforcement Learningd. Neural Network Learning
The behavior of a biological neural network can be captured by a simple model called an artificial neuron or perceptron. Which of the following statements about the perceptron is correct? a. Perceptrons can only be used for linearly separable problems. b. Perceptrons are a type of unsupervised learning algorithm. c. Perceptrons have no activation function. d. Perceptrons can have multiple layers, just like deep neural networks.
Which of the following is the primary characteristic that differentiates an Artificial Neural Network (ANN) from traditional machine learning algorithms?Group of answer choicesRequirement for large datasetsDependence on feature engineeringUse of labeled dataAbility to learn non-linear relationships
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