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I am watching this video lecture by Andrew NG on "Getting Machine Learning Algorithms to work in Practice" and I am stuck at the following two slides:

The problem discussed is as follows: Let's say an SVM outperforms Logistic Regression. How do I know if there is a problem with my 'optimization' or 'optimization function'?

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He suggests to use $\theta_{svm}$ that were learned using SVM in the optimization function of logistic regression. Coincidentally, for both SVM and Logistic Regression, $\theta$s are the weights of linear features. And therefore, I can use $\theta$s learned using SVM into the optimization function of linear regression.

Question: What if $\theta$s are not that straightforward to transfer? For example: what if I want to compare a Neural Network model with Linear Regression?

Timestamped link to the video: https://youtu.be/sQ8T9b-uGVE?t=2703

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