What is the role of parallel computing in machine learning?

What is the role of parallel computing in machine learning?

Parallel computing allows machine learning algorithms to process large datasets more efficiently by dividing tasks across multiple processors.

Can you give me an example of how it's used in training models?

Sure! In training deep learning models, parallel computing can be used to distribute the workload of processing images across multiple GPUs.

What are the benefits of using parallel computing?

The main benefits include reduced training time, improved performance, and the ability to handle larger datasets.

Are there any challenges associated with parallel computing in ML?

Yes, challenges include managing data dependencies, ensuring efficient load balancing, and dealing with communication overhead between processors.