Deep neural networks have a huge advantage: They replace “feature engineering”—a difficult and arduous part of the classic machine learning cycle—with an end-to-end process that automatically learns to extract features. However, finding the right deep learning architecture for your application can be challenging. There are numerous ways you can structure and configure a neural network, using different layer types and sizes, activation functions, and operations. Each architecture has its strengths and weaknesses. And depending on the application and environment in which you want to deploy your neural networks, you might have special requirements, such as memory and computational constraints. The…
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