COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Neurons as nodes
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Synapses as weight
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Neurons as weight
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Synapses as nodes
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Detailed explanation-1: -An artificial neural network consists of a collection of simulated neurons. Each neuron is a node which is connected to other nodes via links that correspond to biological axon-synapse-dendrite connections. Each link has a weight, which determines the strength of one node’s influence on another.
Detailed explanation-2: -What Are the Components of a Neural Network? There are three main components: an input later, a processing layer, and an output layer. The inputs may be weighted based on various criteria.
Detailed explanation-3: -What is Weight (Artificial Neural Network)? Weight is the parameter within a neural network that transforms input data within the network’s hidden layers. A neural network is a series of nodes, or neurons. Within each node is a set of inputs, weight, and a bias value.
Detailed explanation-4: -The Neuron The weight is the intrinsic parameter, the parameter the model has control over in order to get a better fit for the output. When we pass an input into a neuron, we multiply it by its weight, giving us x * w.
Detailed explanation-5: -What is a Synapse in Machine Learning? A synapse is the connection between nodes, or neurons, in an artificial neural network (ANN). Similar to biological brains, the connection is controlled by the strength or amplitude of a connection between both nodes, also called the synaptic weight.