takaisinsyöttöalgoritmin

The takaisinsyöttöalgoritmi, often translated as backpropagation algorithm, is a fundamental method used for training artificial neural networks, particularly multilayer perceptrons. Its primary purpose is to efficiently compute the gradient of the loss function with respect to the weights of the network. This gradient information is then used by an optimization algorithm, such as gradient descent, to adjust the weights and minimize the error between the network's predictions and the actual target values.

The algorithm works by propagating the error signal backward through the network, starting from the output layer and moving towards the input layer. At each layer, the algorithm calculates how much each neuron's output contributed to the overall error. This is achieved using the chain rule of calculus. The gradients computed for each weight are then used to update that weight in the direction that reduces the loss. This iterative process of forward pass, error calculation, and backward pass for gradient computation is repeated over many epochs (complete passes through the training dataset) until the network achieves a satisfactory level of performance.

Backpropagation is crucial for supervised learning tasks in neural networks, enabling them to learn complex patterns and relationships in data. Its development was a significant breakthrough in the field of artificial intelligence, paving the way for more sophisticated deep learning models.