Notes:
Backpropagation is an algorithm used in artificial neural networks to train them and adjust their weights and biases. It is a supervised learning algorithm, which means that it requires a labeled training dataset to learn from.
The basic idea behind backpropagation is to use the known labels for the training data to adjust the weights and biases of the neural network in a way that minimizes the error between the network’s predictions and the true labels. This is done by first feeding the input data through the network to generate predictions, and then using these predictions to calculate the error. The error is then propagated backwards through the network, and the weights and biases are adjusted to reduce the error. This process is repeated until the network reaches a satisfactory level of performance.
Backpropagation is an efficient and widely-used algorithm for training neural networks, and it has been instrumental in the success and popularity of deep learning. It is a highly flexible algorithm that can be used with many different types of neural networks and learning tasks.
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