Artificial Neural Networks

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Artificial Neural Networks (ANN) are a class of computational models inspired by the structure and functioning of biological neural networks in the human brain. They form the backbone of modern machine learning and are used in various AI applications.


An Artificial Neural Network consists of interconnected nodes, known as neurons or nodes, organized into layers: an input layer, one or more hidden layers, and an output layer. In a layer, every neuron is linked to neurons in the neighboring layers via weighted connections. These weights determine the strength of the signal passing from one neuron to another.

During training, the neural network learns from labeled data by adjusting the weights in its connections. The process involves forward propagation, where input data is passed through the network, and backpropagation, where the network’s performance is evaluated, and the weights are updated based on the prediction errors.

Through this learning process, the neural network can capture complex patterns and relationships in the data, enabling it to make predictions and decisions. Neural networks are capable of handling a wide range of tasks, such as image recognition, natural language processing, and playing games, often outperforming traditional algorithms in complex and non-linear problems.

ANNs have undergone significant advancements over the years, leading to the development of Deep Neural Networks (DNNs), which have multiple hidden layers. Deep Learning, powered by DNNs, has revolutionized AI applications and played a vital role in various industries, including healthcare, finance, and autonomous systems.


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