The "Litmus Test" for Inclusion in the Book

[profvjreddi]

Hi folks,

Sometimes, I am asked how we decide what to include in the book and what not to include, given that the field is evolving. To guide this discussion, I've put together a "Litmus Test" that I use as a mental model to guide us through what content we include that is essential, well-established, and widely adopted while also being practical and engaging for readers.

Please read, review, and give feedback.

The Litmus Test

When deciding whether to include a concept or technique in the book, each topic must pass this rigorous "litmus test" to meet relevance, longevity, and industry adoption criteria. The test involves the following key questions:

1. Fundamental Relevance
   Is this concept or technique essential for understanding machine learning systems?

2. Conceptual Soundness
   Is it theoretically robust and well-established in the field?

3. Longevity
   Will this concept or technique remain relevant in the near future?

4. Industry Adoption
   Has it gained broad acceptance and use within the industry?

5. Practical Utility
   Does it have clear applications in real-world systems?

6. Educational Value
   Does it significantly enhance the reader's understanding of machine learning systems?

7. Non-Redundancy
   Is it unique and not overlapping with existing content?

8. Timeliness and Update Cycle
   How current is the information, and how easy will it be to update this content in the future?

9. Supporting Tools
   Are there available tools, libraries, or frameworks that facilitate its implementation?

10. Reader Engagement
    Does the content engage the reader and encourage deeper exploration?

Thoughts?