[D] Doubt with Ovidiu Calin's book Deep learning architectures (2020)

Web: https://www.reddit.com/r/MachineLearning/comments/sdn80s/d_doubt_with_ovidiu_calins_book_deep_learning/

I'm referring to chapter 6 of "Deep learning architectures: a mathematical approach" by Ovidiu Calin (2020).

I admire how well written this book is from a mathematician's perspective. However, in chapter 6, why does the author only considers one input sample for vectorized notations? Where does he discusses the vectorized implementation of DNNs with multiple samples? Or does he not do it at all?

I'm specifically looking for the vectorized implementation of forward and backpropogation with multiple input samples.

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