What is parallelism in deep learning?
An example of an AI chatbot conversation about parallelism in deep learning.
What is parallelism in deep learning?
Parallelism in deep learning refers to the simultaneous execution of multiple operations or tasks to speed up the training process.
How does it improve the performance of neural networks?
It allows for faster computation by distributing tasks across multiple processors or GPUs, reducing training time significantly.
Can you give me an example of how it's implemented?
Sure! Data parallelism is a common approach where the same model is trained on different subsets of data across multiple devices.
What are the challenges associated with parallelism?
Challenges include managing data dependencies, communication overhead between devices, and ensuring load balancing.
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