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Geriye Yayılım Testi
Geri yayılma gradyanlarını, ters bağımlılık sırasını, önbelleğe alınmış ileri değerleri, zincir kuralı çarpımını, paylaşılan yol toplamlarını ve optimize edici ayrımını test edin.
Soru 1 arasında 8
What quantity does backpropagation compute for a neural network's relevant weights?
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Testi çözdüğünüzde cevaplar ve açıklamalar görünür.
- What quantity does backpropagation compute for a neural network's relevant weights?
- Derivatives of the loss with respect to those weights
- A causal percentage of total error uniquely assigned to each weight
- The architecture drawing without any calculation
- A permanently updated set of weights without an optimizer
- Geri yayılımın merkezinde hangi matematik kuralı yer alır?
- Pisagor teoremi
- Bayes teoremi
- Analizin zincir kuralı
- İkinci dereceden formül
- Why can reverse-mode backpropagation compute many parameter derivatives efficiently?
- It avoids all local derivatives
- It reuses graph structure and relevant intermediate results
- It calculates only the output prediction again
- It guarantees every implementation has exactly one forward pass of runtime
- Geri yayılım gradyanları hangi sırayla hesaplar?
- Giriş katmanından çıkış katmanına doğru
- Tüm katmanlar, sıra olmadan aynı anda
- Yalnızca ortadaki gizli katman için
- Çıkış katmanından geriye doğru giriş katmanına doğru
- Does computing backpropagation gradients by itself establish that model weights were updated?
- Yes; every backward computation automatically applies a learning rate
- Yes; gradient values always replace the weights directly
- No; the optimizer update is a separate step
- Only if the network has no parameters
- In a conventional cached-backpropagation workflow, what does the forward computation provide for later derivative calculations?
- Relevant intermediate values that can be retained for the backward computation
- A completed optimizer update without any update rule
- A guarantee that all later evaluation errors disappear
- A permanent deletion of the training objective
- On one composed backpropagation path, local scalar derivatives are three and four. What is their chain-rule product?
- Yedi
- Bir
- Three quarters
- On iki
- A shared parameter has loss-gradient contributions two and five through two paths. What combined contribution is accumulated?
- On
- Yedi
- üç
- Two fifths