In supervised learning techniques, such as backpropagation, the training data consist of vector pairs-an input vector and a target vector.

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In supervised learning, the training data indeed consist of pairs of input vectors and corresponding target vectors, where the input vector is the data the model learns from, and the target vector is the desired output the model aims to predict. Backpropagation is a method used in training neural networks, where these pairs are essential for adjusting the weights of the network to minimize the difference between the actual output and the target output.

In Supervised Learning Techniques, Such as Backpropagation, the Training Data