This is Phillip, our boy cat:
Phillip is a Petbarn rescue and a very noisy creature.
We got him at the age of 2 months and 19 days.
We call him Lip, Flip and Frog.
He likes to climb trees and nibble on fingers. He drinks lots of water and pants whilst playing. He is more of a dog than a cat.
Phillip also enjoys eating eggs, butter and cheese and yoghurt. Any diary really.
This is my first bike, I bought it second hand:
I got it because it fit my needs:
I paid 5.5k AUD for it, and recently had to spend another 2k because the previous owner had let it rust from the rain of the Northern Beaches.
I had also tried to do some work on it and ended up snapping the bolt securing the oil filter cap on the underside of the engine.
#UNSW #undergraduate
This
These days I work in Emacs, but before then I lived in Vim/Tmux for 2 years. My youth though was never this technical nor was it officially programmatic.
I instead spent my time hacking, tinkering and breaking things.
I spent my time in primary school creating hidden folders1 on my student drive to hide games from my teachers. But even earlier than that, I had a penchant for doing the wrong thing.
I was first introduced to this concept by Distrotube (Derek Taylor's) "literate config" files. At the time I was not using emacs and thus all the code I was writing was sparingly commented.
Since then, I have entered a world of Machine Learning and Deep Learning, where suddenly in 4 lines, I can sit atop my high-horse and perform sentiment analysis with tensorflow and keras!
from transformers import pipeline
classifier = pipeline('sentiment-analysis')
prediction = classifier("Donald Knuth was the greatest computer scientist.")[0]
print(prediction)
Device set to use mps:0
{'label': 'POSITIVE', 'score': 0.9997720122337341}
In such an age of abstraction complexity, it becomes paramount to distill what is happening at the last few \((n-k)\) layers.
This blog post has been created to convince you that real-world probability, is in fact Bayesian probability.
Anyone who believes that a frequentist approach is superior may be correct (for that particular example), but it must be said that the Bayesian framework is a superset of this naive and trivial card-playing model of probability.
We are no longer trying to determine the probability of landing a `double-six` dice roll, and rather we are trying to figure out what the probability is that Mia (our cat) will be waiting for us on the porch when we get home.
The human mind is a fickle creature. It is prone to dozens of cognitive biases, and the way it is strengthened, is paradoxical:
To learn, you must forget
The above is a mantra all my students are familiar with, and derives from an understanding of Ebbinghaus' forgetting curve.
Additionally, the human-mind is highly fragile:
And biologically expensive:
Whilst this sword is double-edged; it enables our creativity, capacity for complex thought, reasoning, etc.; the design of this organ leads to largely improper use by its hosts.
Mia is a good cat. Very tame. She's been following me around since she was a blue-eyed baby:
She's a beautiful and largely obedient cat. She was born on the 19th of February 2023, making her almost 2 today.
Mia is not the most intelligent cat, but she's very clean. She is reasonably territorial, and occasionally bitchy.
All things considered, she is a faithful cat that follows me around and responds to being summoned. She also knows how to high-five and stand up.
I ran barefoot for upwards of a year. Here are my thoughts on the
I do not recommend it. Especially in the city where it is likely that your running surface will be concrete or pavement.
It is terrible for your feet, and worse for your knees. It is dangerous and it is painful, please do not do it.
insert toes, perhaps a spoiler?
Disclaimer aside, I shall proceed to justify and describe my experience clocking about 500km(?) barefoot.
This page is for the richness of Babel.
I shall come back to populate it with Biblical history, Emacs inspirations and unpack Jorge's concept of infinity in this blog post.