On the way back from shopping Esme and I stopped to look at the
Eurasian coot family that lives close to our house. Each time we count
the chicks and, luckily, each time the number is six.
The coots are not afraid of humans and actually came quite close to
us, probably expecting to be fed. So we guess someone is feeding them.
In the evening I noticed that the Pterinochilus murinus sling I keep
had opened its burrow; a cork tube halve that the small tarantula had
closed off with webbing and substrate shortly after I got
it.
I put a pre-killed mealworm, Tenebrio molitor, near the entrance of
the burrow and soon after it was gone.
I suspect the tarantula has molted while in hiding.
Most developers early in their careers learn an important
programming principle called Don’t Repeat
Yourself
(DRY). For most engineers this basically means avoid writing the
same lines of code more than once, and realistically that’s the best
place to start when learning how to write efficient code. While DRY
is an important code-writing concept, it’s far from the whole story.
In this post, we’re going to employ one simple natural language
processing (NLP) algorithm known as bag-of-words to classify
messages as ham or spam. Using bag of words and feature engineering
related to NLP, we’ll get hands-on experience on a small dataset for
SMS classification.
This is the first part of my series of articles on learning Haskell,
I’ll be taking you through my journey learning the functional
programming language.
Before we can start writing any Haskell we should first get it setup
on our machine.