Backpropagation is a Neural Network algorithm that is mainly used to process noisy data and detect unrecognized patterns for better clarification. It’s a full-state algorithm and has an iterative nature. As an ANN algorithm, Backpropagation has three layers, Input, hidden, and output layer.
The input layers receive the input values and constraints from the user or the outside environment. After that, the data goes to the Hidden layer where the processing is done. At last, the processed data is transformed into some values or patterns that can be shared using the output layer.
Before processing the data, the following values should be there with the algorithm:
Dataset: The dataset which is going to be used for training a model.
Target Attributes: Output values that an algorithm should achieve after processing the data.
Weights: In a neural network, weights are the parameters that transform input data within the hidden layer.
Biases: At each node, some values called bias are added to the sum calculated(except input nodes).
Backpropagation is simple ANN algorithm that follows a standard approach for training ML models. It doesn’t require high computational performance and is widely used in speed recognition, image processing, and optical character recognition(OCR).
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