taaksepäinvaikutus

Taaksepäinvaikutus, also known as backpropagation, is a fundamental algorithm in artificial neural networks used for training. It is the primary method for calculating the gradient of the loss function with respect to the network's weights. This gradient is then used by an optimization algorithm, such as gradient descent, to update the weights and minimize the error.

The process begins with a forward pass, where input data is fed through the network to produce an output. This output is compared to the desired target output, and the difference is quantified by a loss function. Next, in the backward pass, the error is propagated back through the network, layer by layer. Using the chain rule of calculus, the algorithm calculates how much each weight and bias contributed to the overall error. These calculated gradients indicate the direction and magnitude of change needed for each parameter to reduce the loss. Once these gradients are computed for all parameters, the optimization algorithm adjusts them accordingly. This cycle of forward pass, error calculation, and backward pass with weight updates is repeated iteratively over many training examples until the network achieves a satisfactory level of accuracy. Taaksepäinvaikutus is crucial for enabling neural networks to learn complex patterns from data.