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What is back propagation in AI?

Back propagation is a method used in artificial neural networks to calculate the gradient of the loss function with respect to the weights. It is a key algorithm in training deep learning models, allowing the model to adjust its weights in the direction that minimally reduces the error.

How does back propagation work?

Back propagation works by computing the gradient of the loss function with respect to each weight by the chain rule, computing the gradient backwards through the network. The weights are then updated in the opposite direction of the gradient. This process is repeated many times, with the goal of finding the set of weights that minimizes the loss function.

Back propagation is an efficient way of computing gradients in neural networks, and is a key reason for the success of deep learning.

What are the limitations of back propagation?

While back propagation is a powerful tool for training deep learning models, it has limitations. It requires a lot of data and computational resources, and it can get stuck in local minima. It also assumes that the function being optimized is differentiable, which is not always the case.

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