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What is the purpose of batch normalization in neural networks?
Practice Questions
Q1
What is the purpose of batch normalization in neural networks?
To increase the number of training epochs
To normalize the input features
To stabilize and accelerate training
To reduce the size of the model
Questions & Step-by-Step Solutions
What is the purpose of batch normalization in neural networks?
Steps
Concepts
Step 1: Understand that neural networks learn by adjusting weights based on the data they receive.
Step 2: Realize that the data can vary a lot, which makes learning slow and unstable.
Step 3: Know that batch normalization is a technique used to make the data more consistent.
Step 4: Learn that batch normalization normalizes the inputs to each layer, meaning it adjusts them to have a mean of 0 and a standard deviation of 1.
Step 5: Understand that by normalizing the inputs, the training process becomes faster and more stable.
Step 6: Conclude that batch normalization helps the neural network learn better and more efficiently.
No concepts available.
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