This blog has a really interesting assortment of articles on mathematics and programming. You can use the tags to your right to find topics that interest you, or you may want to have a look at

- the problems I wrote to get your brain working;
- some twitter proofs of mathematical facts.

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In this Pydon't I talk about Python style and I go over some tools you can use to help you remain within a consistent style.

In this Pydon't I show you why refactoring is important and show you how to do it in little steps, so that it doesn't become too overwhelming.

This Pydon't walks you through the usages of the
`__name__`

dunder method and how to use it effectively.

The purpose of this Pydon't is to show you what underscores are used for in Python, and to show you how to write more idiomatic code with them.

In this Pydon't we will take a look at the `reduce`

function,
which used to be a built-in function and is currently
in the `functools`

module.

In this Pydon't we explore what Boolean short-circuiting
for the `and`

and `or`

operators is, and how to use this
functionality to write more expressive code.

In this Pydon't we conclude the slicing trilogy and
take a look at the inner workings of Python slicing,
from the built-in `slice`

type to the dunder method
`__getitem__`

and its siblings.

In this Pydon't we cover advanced topics related to sequence slicing, like (negative) steps, more idiomatic sequence slicing, slice assignment, and slice deletion.

In the fifth article of this short series we will be handling some subtleties that we overlooked in our experiment to classify handwritten digits from the MNIST dataset.

This article covers the basics of sequence slicing in Python and teaches you some idiomatic slicing patterns to write more elegant code.

In this article we use (finite state) automatons to count 698,438,863,898,480,640 passwords in a couple milliseconds.

A short article with all you need to know about sequence indexing in Python – and a bit more.

If you need to access the items of an iterable but also keep
track of their indices, have you considered using `enumerate`

?
Let's talk about another of Python's amazing tools to work
with `for`

loops.

In part 4 of this series we add some unit testing,
improve our tokenizer and implement the primitives `⍴`

and `⍤`

.

10502

`for`

loops are the bread and butter of imperative programming
and Python has some really nice tools to work with them.
If you want to traverse several structures in parallel,
have you considered using `zip`

?

Structural pattern matching is coming in Python 3.10 and
the previous Pydon't explored some interesting use cases
for the new `match`

statement.
This article explores situations for which `match`

isn't the answer.

In the fourth article of this short series we will apply our neural network framework to recognise handwritten digits.

Structural pattern matching is coming in Python 3.10 and this article
explores how to use it to write Pythonic code,
showing the best use cases for the `match`

statement.

The third article of this short series concerns itself with the implementation of the backpropagation algorithm, the usual choice of algorithm used to enable a neural network to learn.