What are the differences between parallel and sequential machine learning?
Parallel machine learning processes multiple tasks simultaneously, while sequential machine learning processes tasks one after another.
Can you give me examples of each approach?
Sure! An example of parallel ML is training multiple models on different data subsets at the same time. Sequential ML could be training a model on a dataset in stages, where each stage depends on the previous one.
What are the advantages of using parallel machine learning?
Parallel ML can significantly reduce training time and improve efficiency, especially with large datasets.
Are there any downsides to parallel machine learning?
Yes, parallel ML can be more complex to implement and may require more resources, such as memory and processing power.
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