Taaksepäinvaikutuksen

Taaksepäinvaikutus, known in English as backpropagation, is a fundamental algorithm in the field of artificial neural networks and machine learning. It is primarily used to train these networks by adjusting their internal parameters, typically weights and biases, in a way that minimizes the difference between the network's predicted output and the actual desired output.

The process begins with a forward pass, where input data is fed through the network, layer by layer, to produce an output. This output is then compared to the target output using a loss function, which quantifies the error. The core of backpropagation lies in the backward pass. Starting from the output layer, the algorithm calculates the gradient of the loss function with respect to each weight and bias in the network. This gradient indicates the direction and magnitude of change needed for each parameter to reduce the error.

These gradients are then propagated backward through the network, layer by layer, using the chain rule of calculus. Once the gradients for all parameters are computed, an optimization algorithm, such as gradient descent, is used to update the weights and biases. This iterative process of forward pass, error calculation, backward pass (gradient computation), and parameter update is repeated many times with different batches of training data until the network achieves a satisfactory level of accuracy. Backpropagation has been instrumental in the development and success of deep learning.