Fastbook Chapter 1 questionnaire solutions (wiki)

In reference to your point about #16: From my understanding, it makes sense to separate the loss function and the optimizer. Before you can optimize and update weights, first you must know how good/bad the current weights are. Therefore, it’s important to recognize that we need a loss function in order to train a model. Point 24 is just following up and making sure we know what a loss function is.

I was wondering if it’s ok to create blogs answering the questions in the book?

I don’t see why not!

Don’t you think Answer of 13 should be this:

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Yes I do :slightly_smiling_face:

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:smiley:

Where do we set the “loss” criteria for the actual optimization method? Is it hardcoded in the “architecture” ? In codes of chapter 1, only metric is set…

Hi,
I created this article from Lesson 1. It has the answers to the first set of questions. Thanks for the awesome course:

" Answers to all these questions are also available on the book’s website." is written in chapter 1 but I can see only your provided answers here in the forum. Do you know where the original answers are located on the book website?

These threads/wikis can be considered the answers

@jeremy would you happen to know if there are similar forum posts that post questionnaire solutions to the subsequent chapters. We have a study group for 2022 that we started and this would be a good resource to use.

Thank you for a wonderful MOOC. Best one I have ever taken!!!

Please find here the wiki thread that summarize all the solutions for each chapter Fastbook questionnaire solutions- MegaThread . And I think you should not @ jeremy except case that he is the only one who can answer the question.

Happy learning !!

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Hi,

What is the Jupyter Notebook online appendix. in:
9. Complete the Jupyter Notebook online appendix.

I promise I gave it all in trying to find it :slight_smile:

@tymtam I came looking for the same thing. Given the order in the questionnaire, I believe this may be the Jupyter Notebook online appendix.