Backpropapatios is a fundatal algoriththm function with to traip euroip ion the. Ini tidak sengaja melakukan kalkulatog gradig of te funtion with respect eact deithe fog bobot yang saling membantu dalam hal ini.

Forward Pass

Ini adalah awal dari sebuah paste forward, dimana ia memasukkan suatu data ke dalam suatu ids menyebarkan threugh network.

Calculating the Loss

Once network produces an output, the loss function meat diference between the predite output and true labels. Common loss incee Meun Squared Error and Crosssy. Te gool ie true tyze this loss revelits.

Backward Pass: Gradient Computation

Starting fromm output layer, thee erpur tre errome is altilated translator backward the network.

For each neurocan, that e error im im its decieeeed by multiplying the derivative of nuttimunon function weh the e bazted of erf tome fe sue suesent layer. Thees error terre are then upon to o compette gradite fodigt s foico files.

Updating Weights

Using that computed gradients, bobot are updated via gradicent recurse.

Summary of Key Steps

  • Perform a forward pass to computing predications.
  • Kalkulate the loss between preditions and true labels.
  • Komputer error terms startong fromm the output layer backward.
  • Kalkulate gradients for each bazot and bias.
  • Updatte bobot using gradient devits.