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Showing 1–50 of 56#learning ×

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2025

The psychology of learning

metamodular.com

My friend told me that (with respect to this phenomenon) people can be roughly divided into two categories that she called perfection-oriented and performance-oriented.

The people in the category perfection-oriented have a natural intellectual curiosity. They are constantly searching for better ways of doing things, new methods, new tools. They search for perfection, but they take pleasure in the search itself, knowing perfectly well that perfection can not be accomplished. To the people in this category, failure is a normal part of the strive for perfection. In fact, failure gives a deeper understanding of why a particular path was unsuccessful, making it possible to avoid similar paths in the future.

The people in the category performance-oriented on the contrary, do not at all strive for perfection. Instead they have a need to achieve performance immediately. Such performance leaves no time for intellectual curiosity. Instead, techniques already known to them must be applied to solve problems. To these people, failure is a disaster whose sole feature is to harm instant performance. Similarly, learning represents the possibility of failure and must thus be avoided if possible. To the people in this category, knowledge in other people also represents a threat. As long as everybody around them use tools, techniques, and methods that they themselves know, they can count on outperforming these other people. But when the people around them start learning different, perhaps better, ways, they must defend themselves. Other people having other knowledge might require learning to keep up with performance, and learning, as we pointed out, increases the risk of failure. One possibility for these people is to discredit other people's knowledge. If done well, it would eliminate the need for the extra effort to learn, which would fit very well with their objectives.

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#learning

2023

Computer Science Education

The Missing Semester of Your CS Education -- You should do this self taught course from MIT whether if you have a degree in CS or not. This is my top recommendation for learning the craft of programming/development.

Classes teach you all about advanced topics within CS, from operating systems to machine learning, but there’s one critical subject that’s rarely covered, and is instead left to students to figure out on their own: proficiency with their tools. We’ll teach you how to master the command-line, use a powerful text editor, use fancy features of version control systems, and much more!

Open Source Society University - computer-science: Path to a free self-taught education in Computer Science! -- links to various MOOCs for pretty much all the topics covered in a CS undergraduate curriculum.

A Functional Introduction To Computer Science (Part I) and Part II

CS Primer Courses (This is a paid course) -- The TL;DR is:

  • Start wherever looks interesting
  • Focus on solving problems
  • Use as much or as little of the problem walkthroughs and explainer videos as you need to keep making progress and having fun
  • Keep going!

notes#computer-science#learning

2022

Spaced Repetition

Learning is Remembering

  1. You can only understand something if it combines less than 4 new pieces of information.
  2. Learning is an alteration in long-term memory -- Ofstead, 2019

Learning is remembering and the easiest way to start learning faster is to stop forgetting the things you learn.

Learn Exponentially

  1. Spaced repetition is a rapid exponential process
  2. reading takes years to double in effectiveness whereas spaced repetition takes only days.0

you can become more effective than anyone thought you could, use spaced repetition to learn exponentially.

notes#learning#technique

2021

2018

Machine Learning

Learning

When people are getting ready to learn machine learning at MSc level, they don’t learn from a course that says “if you don’t understand it it doesn’t matter”, they learn from this course instead. Abu-Mostafa’s Caltech course is probably the best first course that delivers good understanding of machine learning. via

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Browse the State-of-the-Art in Machine Learning | Papers With Code

Pen and Paper Exercises in Machine Learning

This is a collection of (mostly) pen-and-paper exercises in machine learning. The exercises are on the following topics: linear algebra, optimisation, directed graphical models, undirected graphical models, expressive power of graphical models, factor graphs and message passing, inference for hidden Markov models, model-based learning (including ICA and unnormalised models), sampling and Monte-Carlo integration, and variational inference.

See also: machine-learning-books

notes#learning#machine-learning

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