orbit
ebisu
orbit | ebisu | |
---|---|---|
1 | 4 | |
1,811 | 303 | |
0.8% | - | |
7.8 | 0.0 | |
14 days ago | 4 months ago | |
Python | Python | |
GNU General Public License v3.0 or later | The Unlicense |
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orbit
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Uber Releases V1.1 of Orbit: A Python Package to Perform Bayesian Time-Series Analysis and Forecasting
Github: https://github.com/uber/orbit
ebisu
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Anki – Powerful, intelligent flash cards
I really wish something like https://github.com/fasiha/ebisu becomes the norm. That is, the idea of fitting the cards to your time (by prioritising) rather than you having to do everything there software wants.
The only bit missing is some algorithm deciding how often to introduce new cards based on your historical data.
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FSRS: A modern, efficient spaced repetition algorithm
It seems from the description that FSRS still puts an exact review date on each card? This feature was pretty much the reason why I stopped using Anki. I'm not in college and not doing exams, I just want to practice when I feel like it, maybe with large breaks between sessions, and not feel like there's a backlog building up.
I think Anki is a great app, I just wish there was an algorithm that would just randomly sample cards (with probability proportional to how urgently you need to review it) rather than put a review date on them. Something like https://github.com/fasiha/ebisu but available as an Anki plugin (if that supports custom algorithms on mobile yet?) or a similar app with an open format for cards.
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Study Sets: The reason why cards repeat a lot (algorithm explanation)
"GoodNotes uses the Ebisu algorithm for its spaced repetition feature. Ebisu uses a Bayesian model to estimate the probability of remembering a given flashcard, which allows faster adaptation to changes in recall ability. Both algorithms have been shown to be effective in practice, you can learn more about Ebisu at https://fasiha.github.io/ebisu/ "
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Am I using Anki wrong?
This is a fundamental issue with SM-2 and how ease factors work. I personally have my Anki settings set up such that there is no ease factor penalty, though I will be working on porting Ebisu v3 to Anki's v3 scheduler once it's ready, which should finally allow us to have proper adaptive ease factors for cards (on all platforms) without the ease hell problem.
What are some alternatives?
neural_prophet - NeuralProphet: A simple forecasting package
ent.hpp - A header-only library that applies various tests to sequences of bytes stored in files and reports the results of those tests. The class is useful for evaluating pseudorandom number generators for encryption and statistical sampling applications, compression algorithms, and other applications where the information density of a file is of interest.
pmdarima - A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function.
dekki - An ML based spaced repetition algorithm to help you learn faster and remember longer.
ruptures - ruptures: change point detection in Python
option-pricer - Option pricing using Black-Scholes model, Bachelier model, Binomial Trees and Monte Carlo simulation under different stochastic processes
darts - A python library for user-friendly forecasting and anomaly detection on time series.
Midnight - Midnight Score Probabilities using a Monte Carlo Simulation
pyro - Deep universal probabilistic programming with Python and PyTorch
monaco - Quantify uncertainty and sensitivities in your computer models with an industry-grade Monte Carlo library.
fracdiff - Compute fractional differentiation super-fast. Processes time-series to be stationary while preserving memory. cf. "Advances in Financial Machine Learning" by M. Prado.
LearningCards - Simple collaborative online version of learning/flash cards