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Perceptron in Artificial Neural Network Video Lecture | Crash Course for UGC NET Computer science
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FAQs on Perceptron in Artificial Neural Network Video Lecture - Crash Course for UGC NET Computer science
| 1. What is a perceptron in the context of an Artificial Neural Network? |  |
Ans. A perceptron is the simplest form of an artificial neural network. It consists of a single neuron with adjustable weights and a threshold. It takes multiple inputs, applies weights to them, sums them up, and passes the result through an activation function to produce an output.
| 2. How does a perceptron learn in an Artificial Neural Network? | |
Ans. A perceptron learns by adjusting its weights based on the error in its output compared to the desired output. This process is known as training. The weights are updated using a learning algorithm such as the perceptron learning rule or the gradient descent algorithm.
| 3. What is the role of the activation function in a perceptron? | |
Ans. The activation function in a perceptron determines whether the neuron should be activated or not based on the weighted sum of inputs. It introduces non-linearity to the network, enabling it to learn complex patterns and relationships in the data.
| 4. Can a single perceptron solve complex problems? | |
Ans. A single perceptron is limited in its ability to solve complex problems that are not linearly separable. It can only learn linear decision boundaries. To solve more complex problems, multiple perceptrons are used in layers to form a multi-layer perceptron.
| 5. How is a perceptron different from a neural network? | |
Ans. A perceptron is a single neuron with adjustable weights and a threshold, while a neural network consists of multiple layers of neurons interconnected with each other. A neural network can learn more complex patterns and relationships in data compared to a single perceptron.
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Nov 22, 2024 Last updated |
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