Why traditional Machine Learning algorithms fail to produce accurate predictions compared to Deep Learning algorithms?

Hi there,

one reason is that DNNs w/ advanced architectures (like transformers but also architecture w/ convolutional | pooling layers) are designed to perform well on very large datasets and also process highly unstructured data like pictures or videos in a scalable way: basically the more data, the merrier!

Compared to classic ML models, DNNs possess less structure and can learn more complex and abstract relationships given sufficient amount of data.

This thread could be interesting to you since it is about a similar topic:

Best regards

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