Does anyone know if this book is any good? I’m planning to get more familiar with interpretability research, and ‘read a book’ has just appeared in my set of options.
I finished this book (by ‘finish’, I mean read through Chapt 4 through Chapt 7, and read them three times).
Here’s suggestion and what I think:
If you are comfortable reading online, use [this link] to read the GitBook version. A few benefits: errors are adjusted by the author in time, new sections coming from time to time that are only available here in the online version, and lastly, dark-mode possible.
From the TOC you’d see the book is mainly about model-agnostic methods, it introduces most of the model-agnostic concepts that are well-received. The list from this post are mostly for CV or NLP problems. Because my area is to interpret NNs that are trained for tabular data, I find the book very useful.
In the book, each section has a “Pros” and “Cons” of the corresponding method, gives links to the GitHub repo that implements the corresponding method, both R and Python. This is handy.
The illustrations and figures are clear and overall everything’s well-explained.
Downside is, the gradient methods (saliency map), concept detection (TCAV) are not described in detail. I’d recommend reading papers on those specific topics. (Plus, I also noticed that the updates of these chapters were not written by the author of this book. This is understandable as those require people with difference expertise.
A quick look at the table of contents suggests that it’s focused more on model-agnostic methods. I think you’d get a different overview of the field compared to the papers we’ve summarized here, as an fyi.
I think one large area you’d miss out on from reading the book is the recent work on making neural nets more interpretable, or designing more interpretable neural net architectures (e.g. NBDT).
Does anyone know if this book is any good? I’m planning to get more familiar with interpretability research, and ‘read a book’ has just appeared in my set of options.
I finished this book (by ‘finish’, I mean read through Chapt 4 through Chapt 7, and read them three times).
Here’s suggestion and what I think:
If you are comfortable reading online, use [this link] to read the GitBook version. A few benefits: errors are adjusted by the author in time, new sections coming from time to time that are only available here in the online version, and lastly, dark-mode possible.
From the TOC you’d see the book is mainly about model-agnostic methods, it introduces most of the model-agnostic concepts that are well-received. The list from this post are mostly for CV or NLP problems. Because my area is to interpret NNs that are trained for tabular data, I find the book very useful.
In the book, each section has a “Pros” and “Cons” of the corresponding method, gives links to the GitHub repo that implements the corresponding method, both R and Python. This is handy.
The illustrations and figures are clear and overall everything’s well-explained.
Downside is, the gradient methods (saliency map), concept detection (TCAV) are not described in detail. I’d recommend reading papers on those specific topics. (Plus, I also noticed that the updates of these chapters were not written by the author of this book. This is understandable as those require people with difference expertise.
Thanks a bunch for summarizing your thoughts; this is helpful.
I have not read the book, perhaps Peter has.
A quick look at the table of contents suggests that it’s focused more on model-agnostic methods. I think you’d get a different overview of the field compared to the papers we’ve summarized here, as an fyi.
I think one large area you’d miss out on from reading the book is the recent work on making neural nets more interpretable, or designing more interpretable neural net architectures (e.g. NBDT).