What are the downsides of batching in machine learning?

What are the downsides of batching in machine learning?

Batching in machine learning can lead to several downsides, such as increased memory usage and potential loss of granularity in data.

Can you explain how increased memory usage affects model training?

Increased memory usage can limit the size of the model or the dataset you can work with, potentially leading to slower training times.

What do you mean by loss of granularity?

Loss of granularity means that when data is batched, individual data points may not be represented accurately, affecting learning.

Are there any scenarios where batching is still beneficial?

Yes, batching can improve training speed and stability in many cases, especially with large datasets.

How can I mitigate the downsides of batching?

You can experiment with different batch sizes, use techniques like gradient accumulation, or employ more efficient data loading methods.