Links to various repositories of some maybe less well known constraint
solvers. This is mainly so that I can find them again.
I don’t know why this is so difficult. Hugo provides a static
directory where you can put stuff that is directly copied to your web
directory. In theory you can have links [A link]("/file.pdf")
and
if you the file static/file.pdf
in your Hugo project base directory
then all is well.
If your homepage has its root at some other location than the root,
say example.com/Hugo
rather than just example.com
, then this does
not always work. I really don’t understand why.
This is mainly for students on my machine learning course. I use a lot
of Python notebooks either for examples from lectures or for
assignments. While there are many options for hosting python notebooks
for free remotely, but I prefer to do things locally. If you wish to do
the same then please read on. Note that I did home some problems
installing skikit-image
on a new Apple with M1 silicon, but after
upgrading the latest version of the operating system and doing and
updating my homebrew
setup everything works. Note that these
instructions are for using pip
. If you are using an alternative
package manager such as Anaconda
then you are on your own. I know
nothing about setting up Python on a windows machine.
A short tutorial on using the built in state monad. I promise you will not build your own state monad.
Haskell is a really great programming
language. It is elegant, the type system is beautiful, and nowadays
the compiler is quite good. I’ve been using functional languages off
and on for more than 30 years. I studied at the University of
Kent which is the home
Miranda which
is a precursor to Haskell. All this is a warning. I don’t use
Haskell that much. The language has changed a lot since I last used it
regularly, and so my code might not be optimal or idiomatic Haskell.
There are a lot of tutorials and youtube videos out there on using
Naive Bayes for document classification. None of these tutorials are
wrong, but they often hide some subtle points that if you think too
hard about you will get confused. In this posts I want to explain what
is really going on in Naive Bayes for spam classification.
This post assumes that you are already familiar with Bayes’ theorem.
In a lot of my courses I encourage students to use python virtual
environments. Virtual environments are a great way of making sure
that you have the correct version of packages installed.
This is very short cheat sheet on how to set them up. I
will assume that we are using Python 3. Luckily Python 3 has virtual
environments set up. It is all in the
documentation, but then
sometimes people are too lazy to google, or do not know what to google
for. Of course this assumes that you are a command line person. If you
are using some IDE, then you are on your own.
Links and references to Statistics, Probality and Category Theory
Short Review of Sonic Writing by Thor Magnusson.
Some hints and Tips on JavaScipt and Max