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.
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.