The `pybrain.supervised.trainers.BackpropTrainer` is a class in Python's `pybrain` library that is used for training neural networks using the backpropagation algorithm, a popular method for training feedforward neural networks. This trainer specifically focuses on supervised learning, where the network is trained using a labeled dataset to learn the correct output for a given input. The BackpropTrainer uses a gradient descent technique to iteratively adjust the weights and biases of the network in order to minimize the difference between the predicted and actual outputs. By repeatedly propagating errors backwards through the network and adjusting the parameters, the BackpropTrainer gradually improves the network's ability to generalize and make accurate predictions. It provides various customizable options to control the training process, such as learning rate, momentum, batch size, and error function.
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