Learning Path
Question & Answer
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Using ReLU activation functions
Applying batch normalization
Initializing weights with small random values
Implementing dropout layers
Using skip connections in network architecture
Understanding the Answer
Let's break down why this is correct
ReLU lets gradients flow because it does not shrink them. Other options are incorrect because Starting with very small weights makes the signal too weak to move through many layers; Dropout randomly turns off neurons during training, which helps prevent overfitting.
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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