This felt manageable at first but the multi-part structure tripped me up.
Start by defining the vanishing gradient problem and its impact on training deep networks. Then explain the backpropagation mechanism and how gradients can become exponentially small as they propagate backward, focusing on the role of activation functions. Finally, discuss mitigation techniques, emphasizing practical trade-offs and real-world applications.
Pro tip: Relate the vanishing gradient problem to specific architectures like RNNs or very deep CNNs, and mention how techniques like residual connections and batch normalization have enabled training of extremely deep models, showing awareness of modern practices.
Explain that vanishing gradients occur when gradients become exponentially small as they backpropagate through many layers, making early layers train very slowly or not at all.
Describe how backpropagation computes gradients via the chain rule, and how repeated multiplication by small derivatives (e.g., from sigmoid/tanh) causes gradients to shrink.
Compare activation functions: sigmoid and tanh saturate and have derivatives <1, exacerbating vanishing gradients; ReLU and its variants have derivative 1 for positive inputs, mitigating the issue.
Cover techniques like ReLU variants, batch normalization, residual connections, careful weight initialization (e.g., He, Xavier), and gradient clipping (for RNNs).
Highlight that while techniques help, they introduce trade-offs (e.g., dying ReLU, increased complexity) and that the choice depends on architecture and task.
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