Notes

I think taking a course in a subject that you are interested in is never particularly a bad thing.

I remember when using Emacs itself was a huge struggle for me. But now I have just sudo apt install emacs’d this vanilla install and I am already off to the races.

Anyways, I’ll probably slim down this prose at a later date when I find it cringe and too verbose; but for now I am having a terrific time thwacking away at a Drunkdeer A75 Pro (thanks Aarav).

I’ve opted to scribble here as opposed to in a README this time.

It feels a little weird presenting my notes to the world.

Alas, emacs has begun to consume me.

C-x g is magit-status

sections#

Repository Status#

top of window:

Head:     main enh: week49, day1 tutorial, 5 problems
Merge:    origin/main enh: week49, day1 tutorial, 5 problems

Head: current local branch Merge / Rebase: depends what has been done thus far.

also gives info on tags and the number of commits between that and HEAD

Honestly, the diagrams that I wish to reproduce already exist here. Currently this page is in construction and probably will be until I finish my Doctorate.

“Memory is the mother of all wisdom." — Aeschylus

Babbage’s Big Brain#

Memory as a Hierarchy — Not a Monolith#

Hierarchy exists for two intertwined reasons:

1. Physics – Smaller structures are faster and nearer to ALUs but hold less data; larger structures store more but are farther away and thus slower.
2. Economics – Fast memory costs disproportionately more per byte.

An efficient system arranges multiple layers so that > the majority of accesses hit the small, fast part, > while the bulk of bytes reside in the large, cheap part.

deep learning pipeline#

Recall that a Neural Network follows the following construction:

1. Pass data (forward) through model to get predicted values
2. Calculate loss with predicted values against labels
3. Perform backpropagation w.r.t each weight / bias to get the direction in which to move that weight such that it moves closer to the global minima
4. Update parameters with gradients using an optimiser.

momentum#

ball’s pace slows down this makes total fkn sense! if the gradient signs are the same, increasing your confidence in that direction and move further. you want to take less steps over all

Written in Go.

originally written in Bash

Introduction by Josh Medeski (author)#

Link: https://youtu.be/-yX3GjZfb5Y?si=7WP2tkiITxYLGpQH

Just a binary installed with homebrew: .

zoxide is a dependency.

lists out tmux sessions, custom configs, and zoxide paths.

Zoxide sub-video:#

add to zshrc:

zoxide query -l -s

I’ve only just started using this, so the weights are small, and the directories few.

Algorithm#

1. case insensitive
2. same order in real path
3. you need the last term to be the last item in the directory realpath
4. all matches are returned in descending order of their weights

Interactive mode#

dependency: fzf

these are my notes on some courses I have taken at UNSW / have not taken.

primarily this page serves as a place for me to jot down the prescribed texts for the different courses:

math2801#

Introduction to Mathematical Statistics

math2901 ATTACH#

• All of Statistics, by Wasserman
• Mathematical Statistics & Data Analysis by Rice
• A first look at rigorous probability theory, by Rosenthal

math3371 - Numerical Linear Algebra ATTACH#

• Peter J. Olver and Chehrzad Shakiban, Applied Linear Algebra, Second Edition, Springer 2018.

(Digital copy P 512.5/244)

Categorised Openings#

Double King Pawn Openings#

Semi-Open Games#

Double Queen Pawn Openings#

Other Queen Pawn Openings#

Indian Openings#

Flank Openings#

Modern Chess Openings

Fleeting Notes#

• the Caro Kann does not focus on a Kingside

naturally, the credit for the contents here go to refactoring.guru