Chapter VI
Machine Learning
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representation that captures complex relationships and patterns within the data. Each hidden
layer typically consists of multiple nodes, and the number of hidden layers can vary depending
on the complexity of the problem. The information flow through the hidden layers allows the
network to learn and extract meaningful features from the input data.
The connections between nodes in an ANN are represented by weights. These weights determine
the strength and direction of the signal transmitted between nodes. During the training process,
the network adjusts these weights iteratively based on the available input-output pairs.
Activation functions in Artificial Neural Networks (ANN) are crucial for introducing nonlinearity and enabling complex decision-making. They determine the output of nodes or neurons
based on the weighted sum of inputs, adding flexibility and adaptability to the network. Popular
activation functions include the sigmoid function for binary classification, the rectified linear
unit (ReLU) for efficient learning and addressing the vanishing gradient problem, and variants
like Leaky ReLU and PReLU. Other functions like tanh and softmax also find applications in
ANNs. The choice of activation function depends on the problem and desired behavior of the
network, allowing ANNs to effectively model intricate relationships in data and achieve higher
performance across various domains of machine learning and artificial intelligence.
In conclusion, Artificial Neural Networks are a type of machine learning algorithm that mimic
the structure and function of biological neural networks. By utilizing input nodes, hidden layer
nodes, and output nodes, they can learn complex patterns and relationships in data. ANN have
become a fundamental tool in the field of machine learning, enabling the development of
intelligent systems capable of making predictions and decisions (Axelsson T. 2018).
Figure VI-5 Articificial Neural Network Architecture.
Machine Learning
75
representation that captures complex relationships and patterns within the data. Each hidden
layer typically consists of multiple nodes, and the number of hidden layers can vary depending
on the complexity of the problem. The information flow through the hidden layers allows the
network to learn and extract meaningful features from the input data.
The connections between nodes in an ANN are represented by weights. These weights determine
the strength and direction of the signal transmitted between nodes. During the training process,
the network adjusts these weights iteratively based on the available input-output pairs.
Activation functions in Artificial Neural Networks (ANN) are crucial for introducing nonlinearity and enabling complex decision-making. They determine the output of nodes or neurons
based on the weighted sum of inputs, adding flexibility and adaptability to the network. Popular
activation functions include the sigmoid function for binary classification, the rectified linear
unit (ReLU) for efficient learning and addressing the vanishing gradient problem, and variants
like Leaky ReLU and PReLU. Other functions like tanh and softmax also find applications in
ANNs. The choice of activation function depends on the problem and desired behavior of the
network, allowing ANNs to effectively model intricate relationships in data and achieve higher
performance across various domains of machine learning and artificial intelligence.
In conclusion, Artificial Neural Networks are a type of machine learning algorithm that mimic
the structure and function of biological neural networks. By utilizing input nodes, hidden layer
nodes, and output nodes, they can learn complex patterns and relationships in data. ANN have
become a fundamental tool in the field of machine learning, enabling the development of
intelligent systems capable of making predictions and decisions (Axelsson T. 2018).
Figure VI-5 Articificial Neural Network Architecture.