How to choose the right mini-batch size in deep learning

This blog post contains a summary of Andrew Ng’s advice regarding choosing the mini-batch size for gradient descent while training a deep learning model. Fortunately, this hint is not complicated, so the blog post is going to be extremely short ;)

Andrew Ng recommends not using mini-batches if the number of observations is smaller then 2000. In all other cases, he suggests using a power of 2 as the mini-batch size. So the minibatch should be 64, 128, 256, 512, or 1024 elements large.

The most important aspect of the advice is making sure that the mini-batch fits in the CPU/GPU memory! If data fits in CPU/GPU, we can leverage the speed of processor cache, which significantly reduces the time required to train a model!

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Bartosz Mikulski

Bartosz Mikulski

  • Data/MLOps engineer by day
  • DevRel/copywriter by night
  • Python and data engineering trainer
  • Conference speaker
  • Contributed a chapter to the book "97 Things Every Data Engineer Should Know"
  • Twitter: @mikulskibartosz
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