A company wants to deploy a deep learning model in an environment with limited computational resources. What challenge related to deep learning models might they face, and what potential solution could address it?
- Overfitting due to small training datasets
- High memory and processing demands of deep models
- Lack of labeled data for training deep models
- Slow convergence of deep models due to early stopping or small batch sizes
In a resource-constrained environment, one major challenge is the high memory and processing demands of deep learning models. They can be computationally expensive. A potential solution could be model optimization techniques like quantization, pruning, or using smaller network architectures to reduce memory and processing requirements.
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