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I have frequently come across the term "Decision Scientist" & many companies seem to be hiring for this role. Can u shed some light on it?

If related, can u also add "Decision Scientist" to the list of roles in Chapter 1.1 Different Machine Learning Roles

It would help compare & contrast with other related roles

Thanks!

I think in the 5.1.1. Vectors section, where you had

Given n vectors, each of d dimensions, what is their dimensionality span?

You probably meant

what is the dimension of their span

or

what is the dimension of the span of the set of these vectors.

Link for post in this line

Akihiro Matsukawa gave an interesting perspective on the difference between the research scientist and the research engineer with his post: Research Engineering FAQs.

Goes to this link: https://mtskw.com/re/

Which doesn't work

Link to content: https://github.com/chiphuyen/ml-interviews-book/blob/master/contents/1.1.2.2-research-scientist-vs.-research-engineer.md

Error:

In the courses page, the link to the Probability and Statistics course is dead/leads to a 404.

Original link: https://online.stanford.edu/courses/gse-yprobstat-probability-and-statistics

I think that this Coursera course from Stanford: https://www.coursera.org/learn/stanford-statistics is equivalent. The course is free for audit (access to all lectures and reading materials). Raised a PR with this change.

Question number 14 part iv is incomplete in section 7.2 Sampling and creating training data.

The question reads :
Imagine you're training a model to classify skin legion. Only 1....

In 5.2.2 at the bottom, the "curse of big data" blog article has apparently been removed.
I could not find that article anywhere on the internet.

Old link: https://www.analyticbridge.datasciencecentral.com/profiles/blogs/the-curse-of-big-data
Redirects to: https://www.datasciencecentral.com/

Hello,

Thanks for such great book!

I think that some questions in "vectors" section is confusing. For example, in the question 4
"Given two sets of vectors A and B. How do you check that they share the same basis?"

is it meant "how do you check that span of those sets of vector share basis"?

Also in the question 6, what is meaning of L_{norm}?

When attempting to export to PDF, I got an error. It looks like MathJAX-Node dependency is using a deprecated API. This will probably get resolved upstream, but just in case anyone else wants to export, there is a workaround:

Modify node_modules/mathjax-node/lib/mj-single.js on line 540 from

result.speakText = speech.processExpression(result.mml);

to

result.speakText = speech.toSpeech(result.mml);

Source for workaround: kgryte/tex-equation-to-svg#2 (comment)

Command for exporting:

npx honkit pdf ./ ./ml-interviews-book.pdf or npx honkit epub ./ ./ml-interview-book.epub

Question 15 part ii is incomplete in section 7.2
Question reads :
You want to build a model to classify whether a comment is

Hi Chip,

this is a really great book. Thanks for sharing it with the world!

I think there is small typo where A should be swapped with B:

... companies as design partners is a really good sign for A.

In: https://github.com/chiphuyen/ml-interviews-book/blob/master/contents/1.2.1-applications-companies-vs.-tooling-companies.md

Akihiro Matsukawa gave an interesting perspective on the difference between the research scientist and the research engineer with his post: Research Engineering FAQs.

Link to "Research Engineering FAQs" is broken.

Great book, just found a small typo so I thought I will let you know so that you can make this book even better than it already is:

In this line: "Some just add an interview specially focused on ML on top of their existing interview process for ML, making their MLE process a bit longer than their SWE process.", the highlighted word ML should be SWE in my opinion.

In the before last paragraph, you wrote

When negotiating with big companies, it’s easier to negotiate equity grants and bonuses than base salary, since base salary.

You seem to have had a thought that didn't finish at the end of that sentence.

Thank you for the great book!

Hey,

In https://huyenchip.com/ml-interviews-book/contents/1.1.3.1-production-cycle.html there is written:

The main skills listed at each step below will upset many people, as any attempt to simplify a complex, nuanced topic into a few sentences would.

Would ... what?