In machine learning, Neurons serve as distinct units within a single hidden layer of a Neural Network. Each Neuron functions by executing a two-step action: It calculates the weighted sum of the input values, which have been multiplied by their associated weights, and then passes the resultant output as an input value to an activation function.
Neurons in the first hidden layer accept inputs from the feature values in the input layer, while those in further hidden layers receive inputs from neurons in the preceding hidden layer. By doing so, they mimic the behavior of actual neurons found in brains and other parts of the nervous system, making them a powerful tool for machine learning applications.
Functions of Neuron
- Neurons process input data which is fed into an Artificial Neural Network (ANN). Each neuron receives data from one or more neurons and produces an output that is passed onto other neurons.
- Neurons enable face recognition by identifying faces or detecting objects in images. Neurons extract relevant patterns and characteristics from input data.
- Neurons are applied in text classification, where, for instance, a given email is classified as spam or not based on its characteristics.
- Neurons make predictions based on historical data, such as stock prices or customer behavior.
- Neurons optimize the performance of ANNs by adjusting the weights and biases of neurons to reduce error and maximize accuracy.
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