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Choose Wisely: Model Complexity and Task Suitability
When addressing complicated tasks like object detection, companies often require more complex models and larger datasets for training, highlighting the necessity of fine-tuning pre-trained models rather than starting from scratch. This approach is beneficial when facing limitations in data quantity, labeling quality, or computational resources. In contrast, simpler tasks such as sentiment analysis or time series forecasting can often be efficiently managed with less complex models and smaller datasets, allowing for quick training times and maintaining high performance without needing extensive resources.
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