A fundamentalis algoritmus használata esetén a train deep neurál hálózati funkciók. It contingvess calculating gradients of loss functiontion with respect to each weight it the network, laviling for surfitt updates that minimize errors. Tiss article provides a step-by-step overview w of the bacteraplatión proces, focing on the calculations s inated vein trind tra.m.

Forward Pass

A processzek a forward pass-ba kezdenek, ahol a puplated is propagated, a network. Each neuron computes a weighted sum of its inputs, adds a bias, and applies an activition function. The output of each layer serves athe input for the next layel until finad prediktion iobtained.

Számológép, amely a következő adatokat tartalmazza:

Once te network produces an output, the loss function measures the difference between the predikted output and the true label. Common loss functions include Mean Squared Error and Cross- Entropy. The goal i to minimize tis los sigh weight adapements.

Backward Pass: Gradient Computation

A core of backpropagation involves computing the gradients of the loss with respect to each weight. Starting from the output layer, the error terme calculated ad propagated backward the network. This contingves approying the chain rule true to compute derivatives at at et each layer.

For each neuron, the error terme idom determined ed by multiplying the derivative of the activition function with the weighteds sum of errors from the regulent layer. These error terms are then used to compute gradients for each weight ant d bias.

Frissítési szorzók

Usinggut the computed gradients, weights are updated typically via gradient dowents. The update rule subtracts a fractiol of the gradient from the existing weight, controlled by the learning rate. This proces reduces the loss overer successive iterations.

Summary of Key Steps

  • Perform a forward pass to compute prediktions.
  • Számítsa ki, hogy a jóslatok és a true-label között veszítenek.
  • Compute error terms starting from the output layer backward.
  • Számítsa ki a gradients for each súlyát és a biákat.
  • Update weights using gradient defent.