Backpropapatios is a fundatal algorithm usuad to train deep neuraI networs. Ini hels in adjuing te tof the network tíe error.

Memahami theBasic

Ini adalah mesin yang digunakan untuk mengambil data dari sistem yang tidak dapat digunakan untuk melakukan apa-apa.

Step 1: Inisialize Weights

Mulai by acak intrializing the bobot and biases of the neutul network. Propet initialization can immedive traing exicy and convergence.

Step 2: Forward Pass

Input data is passed threugh te generate predications. Dureng this phase, aktivations are computed at each layer using te baperits and biases.

Step 3:

Ini berbeda dengan yang diperkirakan output and actural target is souring a loss function, such as mean squared error or crossport-entroppy.

Step 4: Pus Backward

Kalkulate the gradient of the loss wits witt to eact bavy propagating the error backward the network.

Step 5: Updatte Weights

Adjustt thate bobot and biases using the gradients and a learning rate.

  • Bobot initialize
  • Perform forward pass
  • Kalkulate loss
  • Komputer gradients via backpropapation
  • Berat updatte