Backpropation is a currental algoritm used to train deep neural networks. It helps in settingg thoe healths of the network to minimize thee error. This guide provides a step process to implement backpropagation effectively.

Understanding thee Basics

Backpropagation impeves calculating thee gradient of thes loss function with respect to o each heacht in the network. This process uses thoe chain rule of calcuus to profilate errors backward from the output layer to te input layer.

Step 1: Initialize Weights

Start by randomizoval inicializing the váhy and biases of the neural network. Proper initialization can improvize training effectency and convergence.

Step 2: Forward Pass

Input data is passed protingh thee network to generate predictions. During this phhase, activations are computed at each layer using thee current heavelts and biases.

Step 3: Komputní ztráty

To je rozdíl mezi tím, že predicted output a thee actual actualt is measured using a loss funktion, such as mean squared error or cross-entropy.

Step 4: Backward Pass

Calculate te gradient of thes loss with respect to each heacht by profitating the error backward courgh the network. This implives computing derivatives at each laier.

Step 5: Update Weighs

Adjust the eigh ts and biases using the gradients and a learning rate. This step minimizes the loss function and improvizes the network 's executive.

  • Inicializované váhy
  • Perform forward pass
  • Vypočítané losy
  • Compute gradients via backpropagation
  • Aktualizované váhy