Learning Path
Question & Answer
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Implement batch normalization layers to stabilize the learning process
Increase the learning rate to speed up convergence
Reduce the number of training samples to avoid overfitting
Use a simpler model architecture with fewer parameters
Understanding the Answer
Let's break down why this is correct
Batch normalization normalizes the inputs to each layer, keeping values in a stable range. Other options are incorrect because Increasing the learning rate does not fix gradient flow; Reducing training samples does not affect how gradients move through layers.
Key Concepts
Vanishing/Exploding Gradients Problem
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Deep Dive: Vanishing/Exploding Gradients Problem
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Definition
The vanishing/exploding gradients problem poses a challenge in training deep neural networks, hindering convergence during optimization. Techniques such as normalized initialization and intermediate normalization layers have been developed to mitigate this issue and enable the training of deep networks with improved convergence rates.
Topic Definition
The vanishing/exploding gradients problem poses a challenge in training deep neural networks, hindering convergence during optimization. Techniques such as normalized initialization and intermediate normalization layers have been developed to mitigate this issue and enable the training of deep networks with improved convergence rates.
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