← Pinduoduo Interview Insights
I started with the core idea fine, gradients shrinking as they propagate back through layers so early layers barely update.
Start by defining the vanishing gradient problem and explaining its cause through backpropagation and activation functions. Then discuss common mitigation techniques, emphasizing their trade-offs and practical considerations. Conclude with a brief example or mention of how this impacts real-world model design.
Pro tip: Mention that while vanishing gradients are often associated with sigmoid/tanh activations, they can also occur in very deep ReLU networks due to poor initialization or architecture, showing depth of understanding.
Explain that vanishing gradients occur when gradients become exponentially small as they propagate back through layers, making early layers train very slowly or not at all.
Describe how repeated multiplication of small derivatives (e.g., from sigmoid/tanh) during backpropagation leads to exponentially decaying gradients.
Cover techniques like ReLU activations, batch normalization, residual connections, and proper weight initialization (e.g., Xavier/He).
Mention that some solutions introduce new issues (e.g., ReLU can cause dying neurons) and that architecture choices depend on the specific task.
Summarize how these techniques enable training of very deep networks and mention any real-world examples or experiences.
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