Week 3 Quiz - Potential Contradiction

Course Q&A Deep Learning Specialization Neural Networks and Deep Learning
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I am not sure what they mean by that. I can think of two theories:

  1. They really mean the “feature vectors” in the W^{[1]} case. Those are legitimately column vectors.
  2. They mean it in the sense explained in the lectures where Prof Ng treats the rows of W as the transpose of the w weight vectors as we had them in Logistic Regression. So they mean the w vectors before the transpose.

If it is theory 2), then it is explained on this thread.

In either case, I think that way of explaining their answer is unnecessarily confusing. I will research more and then file a bug asking them to make this more clear.

Thanks for pointing this out.

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