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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