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[D] How do you standardize/scale data for multi-input neural networks?

Books teach us that we have to standardize or scale data (when features have non-comparable scales) before feeding a neural network (or other algorithms such as linear or logistic regressions).

When you have multi-input neural networks (for example you have some “dense” inputs for tabular/structured data and some convolutional layers for unstructured data such as images… and then at some point you concatenate their output), how do you standardize/scale data? You have totally different input data, different NN layers and then at some point you merge them (e.g. https://keras.io/getting-started/functional-api-guide/#multi-input-and-multi-output-models). How do you handle data normalization in this case?

submitted by /u/ekerazha
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.