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2 changes: 1 addition & 1 deletion 2- Improving Deep Neural Networks/Readme.md
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Expand Up @@ -422,7 +422,7 @@ Implications of L2-regularization on:
1. faster learning:
- you have the vectorization advantage
- make progress without waiting to process the entire training set
2. doesn't always exactly converge (oscelates in a very small region, but you can reduce learning rate)
2. doesn't always exactly converge (oscillate in a very small region, but you can reduce learning rate)
- Guidelines for choosing mini-batch size:
1. If small training set (< 2000 examples) - use batch gradient descent.
2. It has to be a power of 2 (because of the way computer memory is layed out and accessed, sometimes your code runs faster if your mini-batch size is a power of 2):
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