The modularity tradeoff
This chapter has shown that it is possible, and often useful, to aid a machine learning model with some rule-based system. You might also have noticed that the images in the dataset were all cropped to show only one plant.
While we could have built a model to locate and classify the plants for us, in addition to classifying it, we could have also built a system that would output the treatment a plant should directly receive. This begs the question of how modular we should make our systems.
End-to-end deep learning was all the rage for several years. If given a huge amount of data, a deep learning model can learn what would otherwise have taken a system with many components much longer to learn. However, end-to-end deep learning does have several drawbacks:
End-to-end deep learning needs huge amounts of data. Because models have so many parameters, a large amount of data is needed in order to avoid overfitting.
End-to-end deep learning is hard to debug. If you replace...
