Backpropagation — ML Concept & Coding Problems | GRADuate

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Backpropagation

Hard

The chain rule made computational. Learn how gradients flow backward through a network to train every parameter simultaneously.

Learning Objectives

→Derive gradients via the chain rule
→Implement a backward pass step by step
→Identify vanishing/exploding gradient causes
→Debug gradient flow in deep networks

Practice

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The optional multiple-choice concept check tracks your understanding. Browse the coding problems below, then sign in when you're ready to solve them.

Coding Problems (4)

MSE Loss Gradient

~15 min· Hard

Sigmoid Gradient (Backprop)

~15 min· Hard

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ReLU Backward Pass

~10 min· Easy

Linear Layer Weight Gradient

~15 min· Medium