Guide overview: Utilized Math for Safety
I not too long ago acquired an e-mail from Briana Blackwell from No Starch Press‘s advertising and marketing division,
asking if I’d be thinking about reviewing a free copy of
the early access
model of Applied Math for Security: A Pythonic Introduction to Graph Theory and Computational Geometry
by Daniel Reilly. It is mainly a
showcase of what could be carried out with numpy,
SciPy, pandas,
NetworkX, dlib, … and a few gentle
utilized arithmetic.
It is a self-contained e book, beginning with learn how to set up Python and the
required libraries, to the code to copy-paste to get the introduced outcomes.
It showcases the standard suspects of “cools issues one can do with python and a few utilized maths”:
Voronoi Diagrams,
Graphs properties,
Facial recognition,
Monte Carlo, …
with your complete final chapter devoted to the Art gallery Problem.
I actually favored that earlier than every chapter, ethics and a little bit of historic context
of every issues have been mentioned, and what questions ought to one ask themselves
when waddling into these territories. Every part is neatly launched and introduced,
and everybody ought to have the ability to implement the algorithms, perceive what’s
occurring and really feel warn and fuzzy as the whole lot falls into place. However this is not
a e book about safety; it is a e book about some cool utilized maths rules,
introduced as options to some security-related situations.
All mathematical formulation are dropped with none accompanying proofs,
which is sensible within the context of the e book,
and sources/supplies are offered on the finish of the chapters for the
curious minds anyway, however my PTSD from my college math programs was nonetheless triggered.
Sadly, the e book nonetheless has a few minor shortcomings and eyebrow elevating
statements:
- the Python code is not all the time good, optimum or idiomatic, however the writer explicitly
said that this wasn’t a e book about studying python, and it’d
even make reader really feel sensible, so I assume it is okay; - the e book typically felt a bit dated concerning pc safety: it mentions Armitage (final commit in 2016),
talks about implants behaviours from the early 00s, … - it mentions Stanley Milgram‘s
Small-world experiment (which as been debunked as pseudo-science),
and says that it may be defined by the Preferential Attachment
(which is unrelated). This made me doubt the accuracy of the remainder
of the e book; - a careless parallel between potential/kinetic vitality and potential/kinetic
data. Sadly, kinetic information
is a well-defined psychology time period, making the comparability complicated.
Aside from these, it is a fairly strong e book.
Conclusion
A very nice introduction to graph idea and computational
geometry for individuals who know a little bit of Python and and not using a mathematical
background. I believe I might suggest it to anybody who favored the wonderful
Python for Kids
and need to dig deeper.