Unlocking Efficient AI: The Hidden Power of Data Sampling
When it comes to maximizing the impact of deep learning models, two popular strategies often come to mind: scaling model performance via distributed training and optimizing data sampling techniques. While both approaches have their merits, a closer examination reveals that the latter might be the unsung hero of efficient AI.
Distributed Training: A Computational Band-Aid
Distributed training involves spreading the workload across multiple machines to speed up training times. This approach can be effective for large-scale models, but it comes with significant computational costs and logistical challenges. Moreover, it doesn't address the underlying issue of data distribution and class imbalance, which can lead to biased models.
Data Sampling Techniques: The Secret to Efficient AI
Optimizing data sampling techniques, on the other hand, focuses on reducing the computational requirements of edge cases, such as ra...
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