Ann Architectures | Deep Learning

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A team compares two networks on a tabular dataset with 100 features and 50,000 training samples: Network A has 1 hidden layer with 1024 units; Network B has 4 hidden layers with 256 units each. Both have approximately the same total parameter count. Network B consistently outperforms Network A. A junior engineer concludes "more layers is always better." What is the accurate explanation and when would this conclusion fail?

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Select Topic

easyAnn Architectures

A team compares two networks on a tabular dataset with 100 features and 50,000 training samples: Network A has 1 hidden layer with 1024 units; Network B has 4 hidden layers with 256 units each. Both have approximately the same total parameter count. Network B consistently outperforms Network A. A junior engineer concludes "more layers is always better." What is the accurate explanation and when would this conclusion fail?