Learning Paths: Understanding and Implementing Backpropagation in Neural Networks

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Backpropagation, short for “backward propagation of errors,” is a supervised learning algorithm commonly used for training artificial neural networks. It’s a form of gradient-based optimization that adjusts the weights of a neural network in order to minimize the error between its predicted output and the actual target values.

I. Key Concepts in Backpropagation:

1. Feedforward Pass:

• During the feedforward pass, the input data is propagated through the network layer by layer, generating predictions.

2. Compute Loss:

• The difference between the predicted output and the true target values is quantified using a loss function. Common loss functions include mean squared error for regression tasks and categorical cross-entropy for classification tasks.

3. Backward Pass (Backpropagation):

• The backward pass involves computing the gradient of the loss with respect to the weights of the network. This is done using the chain rule of calculus.

4. Gradient Descent:

• The computed gradients are used to update the weights of the network in…