Taaksepäinlevittelyllä

Taaksepäinlevittelyllä, often translated as "backward propagation" or "backpropagation," is a fundamental algorithm used in training artificial neural networks. It is an efficient method for computing 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 improve the network's performance.

The process begins after a forward pass, where input data is fed through the network, and an output is produced. The difference between the network's output and the desired output is quantified by a loss function. Backpropagation then works backward from the output layer to the input layer. At each layer, it calculates how much each weight contributed to the overall error. This is achieved by applying the chain rule of calculus to propagate the error gradient layer by layer.

Essentially, taaksepäinlevittelyllä determines the "blame" for the error on each connection (weight) in the network. By understanding these contributions, the network can learn which connections need to be strengthened or weakened to reduce the error in future predictions. This iterative process of forward pass, error calculation, and backward propagation of gradients is the core mechanism by which most modern neural networks learn from data. It is a crucial component of supervised learning tasks in deep learning.