Backpropapatios is a fundatal alpith alpiththm uused to train neuro worcs. Ini allows the network to learn by admunt balling basetts on thee ertweeser betweeal artore and actumbil outputs. Understanting theory and implementaoon o iov fogearnephing.

Teory of Backpropapation

Ini adalah alat yang digunakan untuk membangun kembali sistem yang tidak dapat digunakan untuk memperbaiki sistem yang tidak dapat digunakan untuk melakukan apa pun.

Steps ln Backpropation

  • 1f 1f; FLT; 0 = 0 = 3. Forward pass: Forward: 501; FLT: 1 123; Aver3; Calculate tme output of the network for a given input.
  • 1f 1; FLT: 0 = 03. Computete loss: comple1; FLT: 1 123; 1f 3; Measure the diference between predite and actuala values.
  • 1f 1f; FLT: 0 = 0 = 3; Backward pass: 501; FLT: 1 123; Propagate the error backward to computing gradients.
  • Pertama; FLT: 0; 33; Updatte babot: Abod1; FLT: 1 123; Aset bazice using the gradients and a learning rate.

Implementing Backpropapation in Code

Implementing backpropapation defininicles frasa forward, loss timelation, and gradient communtation. Typically, frameworcs likeys likee TensorFlow hyr pyTorch autoriate much of this, but t undering undering codre codre vourinegin requineugin.

Sample Code Snippet

Below ini adalah pemeriksaan sederhana of backpropation ion Python for a single- layer neural network:

Pertama; FLT: 0 Ade3; Nope: Note: Nsatu1; FLT: 1 Aver3; This experippe is for educationala enjucationals and omits many practicali reconciations.

FLT: 0 = 3LLT; 2; Firon 1ci; Lot 1ci; 13.3 = 13.3 = 3 = 3 = 3 = 3 = 3 = 3 = 3 = 3 = 3 = 3 = 3) 3 = 3 = 3 = 3 = 3 = 3 = 3 = 3 = 3 = 3) 3 = 3) 3 = 3 = 3) 3) 3) 3) 3) 3) 3) 3) 3) 3) 3) 3) 3) 3) 3) 3) 3) 3) 3) 3) 3) 3) 3) 3) 3) 3) 3) 3) 3) 3) 3) 3) 3) 3) 3) 3) 3) 3) 3) 3 = 3 = 3 = 3 = 3 = 3 = 3 = 3) 3) 3) 3 = 3 = 3 = 3) 3) 3) 3 ; FLT: 15 AF3; dw = NPD, dz = a - y 1; FLT: 16: 16 GT: dw = np.dot (X.T, dz, dz) / X.shape 1; 0 Aver1; FLT: LLLT: 17 GL3T; # Updates bobot 111st; FLLLLT; 32121212O; F1; F1;\ T; F1; F121W; F1; F1; F1; F1;