Question

Consider an Artificial Neural Network (ANN) with four input nodes, two hidden nodes, and one output node. The ANN is used to classify submarines as either belonging to the American Virginia class or not. The activation function used for both the hidden layer and output layer is the sigmoid function. The ANN is trained using a dataset of submarines, where each submarine is described by the following features: Feature 1: displacement (in tons) Feature 2: length (in meters) Feature 3: maximum speed (in knots) Feature 4: weapon capacity (number of missiles) The weights and biases for each node are as follows: Input layer: Node 1: weight = 0.1, bias = 0.2 Node 2: weight = 0.3, bias = -0.1 Node 3: weight = 0.5, bias = 0.3 Node 4: weight = -0.2, bias = 0.4 Hidden layer: Node 1: weight = 0.4, bias = 0.1 Node 2: weight = -0.5, bias = 0.2 Output layer: Node 1: weight = 0.6, bias = 0.5 The sigmoid function is defined as follows: sigmoid(x) = 1 / (1 + exp(-x)) Suppose we have a submarine with the following features: Displacement: 7500 tons Length: 115 meters Maximum speed: 30 knots Weapon capacity: 30 missiles Using this ANN, determine the probability that this submarine belongs to the American Virginia class.

          Consider an Artificial Neural Network (ANN) with four input nodes, two hidden nodes, and one output node. The ANN is used to
classify submarines as either belonging to the American Virginia class or not. The activation function used for both the hidden
layer and output layer is the sigmoid function.
The ANN is trained using a dataset of submarines, where each submarine is described by the following features:
Feature 1: displacement (in tons) Feature 2: length (in meters) Feature 3: maximum speed (in knots) Feature 4: weapon capacity
(number of missiles)
The weights and biases for each node are as follows:
Input layer: Node 1: weight = 0.1, bias = 0.2 Node 2: weight = 0.3, bias = -0.1 Node 3: weight = 0.5, bias = 0.3 Node 4: weight =
-0.2, bias = 0.4
Hidden layer: Node 1: weight = 0.4, bias = 0.1 Node 2: weight = -0.5, bias = 0.2
Output layer: Node 1: weight = 0.6, bias = 0.5
The sigmoid function is defined as follows:
sigmoid(x) = 1 / (1 + exp(-x))
Suppose we have a submarine with the following features:
Displacement: 7500 tons Length: 115 meters Maximum speed: 30 knots Weapon capacity: 30 missiles
Using this ANN, determine the probability that this submarine belongs to the American Virginia class.

Consider an Artificial Neural Network (ANN) with four input nodes, two hidden nodes, and one output node. The ANN is used to
classify submarines as either belonging to the American Virginia class or not. The activation function used for both the hidden
layer and output layer is the sigmoid function.
The ANN is trained using a dataset of submarines, where each submarine is described by the following features:
Feature 1: displacement (in tons) Feature 2: length (in meters) Feature 3: maximum speed (in knots) Feature 4: weapon capacity
(number of missiles)
The weights and biases for each node are as follows:
Input layer: Node 1: weight = 0.1, bias = 0.2 Node 2: weight = 0.3, bias = -0.1 Node 3: weight = 0.5, bias = 0.3 Node 4: weight =
-0.2, bias = 0.4
Hidden layer: Node 1: weight = 0.4, bias = 0.1 Node 2: weight = -0.5, bias = 0.2
Output layer: Node 1: weight = 0.6, bias = 0.5
The sigmoid function is defined as follows:
sigmoid(x) = 1 / (1 + exp(-x))
Suppose we have a submarine with the following features:
Displacement: 7500 tons Length: 115 meters Maximum speed: 30 knots Weapon capacity: 30 missiles
Using this ANN, determine the probability that this submarine belongs to the American Virginia class.

Added by Alejandro S.

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