How does Deep Learning model complexity typically compare to traditional Machine Learning models, and what are the implications of this?
- Less complex and easier to train
- Less complex and requires less data
- More complex and easier to interpret
- More complex and requires more data and computation
Deep Learning models are typically more complex, requiring more data and computational resources, which can make training and tuning more challenging.
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