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#machine-learning.
Posts on the Alpisto engineering blog filed under#machine-learning.
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- 01#flybeeper#machine-learning#paraglideml
How far, not whether: training a flyability model on five million flights
The technical story of the Flyability forecast on the FlyBeeper map: five million XContest flights, GFS weather, cumulative XC-distance probabilities, confident learning against self-reported logs, isotonic calibration, and an honest evaluation protocol. From a binary classifier and an attention network to three gradient-boosted trees that ship inside the package and refresh a map layer four times a day.
21 min read - 02#flybeeper#machine-learning#reinforcement-learning
Machine Learning for Thermal-Soaring Optimisation: a Survey of Approaches, Architectures and Open Problems
A survey of deep learning and reinforcement learning for autonomous thermal soaring — LSTM/GRU/TCN/Transformer architectures, POMDP and PPO/TD3 formulations, Kalman + ML hybrid filtering, and a concrete five-stage plan for an on-board AI co-pilot for paragliders.
17 min read