Back Propagation

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Backpropagation is an essential concept in machine learning and neural networks. It is a learning algorithm used to train neural networks by adjusting the weights and biases of the network based on the error of the predicted output compared to the actual output. The name “backpropagation” comes from the fact that the error is propagated backwards through the network, from the output layer to the input layer.

In a neural network, information flows forward from the input layer, through the hidden layers, to the output layer. During training, the predicted output of the network is compared to the actual output, and an error is calculated. Backpropagation works by calculating the contribution of each weight and bias to the total error of the network. This is done by applying the chain rule of calculus, which allows us to calculate the partial derivatives of the error with respect to each weight and bias.

Once the partial derivatives are calculated, the weights and biases are updated using an optimization algorithm, such as gradient descent. The goal of backpropagation is to minimize the error of the network by iteratively updating the weights and biases. This process is repeated for each input in the training set until the network converges to a set of weights and biases that produces the desired output for a given input.

Backpropagation is a crucial step in training neural networks and allows them to learn from data and improve their predictions. It enables the network to adjust its internal parameters based on the feedback provided by comparing the predicted outputs with the actual outputs, ultimately leading to more accurate and reliable predictions.

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