free-spaced-repetition-scheduler
yomitan
| free-spaced-repetition-scheduler | yomitan | |
|---|---|---|
| 11 | 2 | |
| 260 | 772 | |
| 5.8% | 13.5% | |
| 5.3 | 9.7 | |
| 23 days ago | 4 days ago | |
| Python | JavaScript | |
| MIT License | GNU General Public License v3.0 only |
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free-spaced-repetition-scheduler
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Anki – Powerful, intelligent flash cards
... https://github.com/open-spaced-repetition/fsrs4anki/wiki/The... ...
I'm not sure I believe we understand our own learning/memory anything like enough for this not to be total pseudoscience? Reminds me of A Beautiful Mind.
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FSRS: A modern, efficient spaced repetition algorithm
Libraries: https://github.com/open-spaced-repetition/free-spaced-repeti...
All are MIT licensed I believe, Anki is primarily AGPL
- The FSRS (Free Spaced Repetition Scheduler) Algorithm
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FSRS explained, part 1: What it is and how it works
Just read the wiki ¯\_(ツ)_/¯
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FSRS is supported in AnkiMobile now!
The model used by FSRS: https://github.com/open-spaced-repetition/fsrs4anki/wiki/Free-Spaced-Repetition-Scheduler
- How to use the next-generation spaced repetition algorithm FSRS on Anki?
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How did I publish a paper in ACMKDD as an undergraduate? A fantastic research experience on spaced repetition algorithm. Open source the code and dataset.
Also, sorry for nitpicking, but I just checked the code here, and I saw that you changed the formula for post-lapse stability, but you didn't update the formula in the description here.
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New progress in implementing the custom algorithm.
I have another one, also about difficulty. Look at the long "Stability updating formula after successful review" formula, there is a term D-b. This seems very counter-intuitive. A large value of D corresponds to an easy card, but thanks to that formula it will produce a very small change in stability. A small value of D corresponds to a difficult card, but according to that formula it will change stability a lot.
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Implement a new spaced repetition algorithm based on anki custom scheduling.
So I checked this and it seems that there's a whole bunch of different parameters (initial difficulty, initial stability, etc), and a lot of them are constant. So the next step would be to use some kind of optimization algorithm, like gradient descent, to optimize those parameters based on user's review history, right?
yomitan
- Anki – Powerful, intelligent flash cards
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can you recommend a good ebook reader hardware?
You can jailbreak the kindle(not needed on the kobo) to install koreader which is good but that won't beat alternative under an android device like the Boox Page: Anki + AnkiconnectAndroid + kiwi + yomitan/yezichak
What are some alternatives?
fsrs4anki - A modern Anki custom scheduling based on Free Spaced Repetition Scheduler algorithm
Anki-Android - AnkiDroid: Anki flashcards on Android. Your secret trick to achieve superhuman information retention.
highlight-search-results - Highlight Search Results in the Browser add-on for Anki
yomichan-with-example-sentences - Japanese pop-up dictionary extension for Chrome and Firefox. With example sentences.
fsrs4anki-helper - An Anki add-on that reschedules all cards via FSRS4Anki scheduler
ebisu - Public-domain Python library for flashcard quiz scheduling using Bayesian statistics. (JavaScript, Java, Dart, and other ports available!)
autoEaseFactor - Adjust ease factors in Anki based off of performance in order to hit a target success rate.
flashcards-obsidian - 🎴 An Anki plugin for Obsidian.md
SSP-MMC - A Stochastic Shortest Path Algorithm for Optimizing Spaced Repetition Scheduling
AnkiconnectAndroid - Unofficial Ankiconnect for Android (i.e. for Yomichan)
tatoeba-to-anki - Creates Anki Flash cards from Tatoeba sentences, ordering them by difficulty and downloading audio
android - This repository is archived, and is now transferred to https://github.com/kiwibrowser/src