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karandeeps18/OptionpricingUsingDNN

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We are performing an experiment to check if the machine learning models can learn BlackScholes option pricing PDE. It is a classical function approximation problem. We are treating the option as plain European option and the model is initially trained on synthetic BS-call-option data.

active 2024-11-17 → 2025-04-02 (UTC)

Complete coverage27,854 / 27,854 hourly files (100%) · 2 absent upstream2023-08-15 → 2026-10-18 (UTC)
Events
8
Pushes
1
Pull requests
1
Issues
0
Stars
0
Forks
0

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Line chart, 137 days from 2024-11-17 to 2025-04-02. Pushes: 1 total, peak 1 in a day. Pull requests: 1 total, peak 1 in a day. Issues: 0 total, peak 0 in a day. Comments: 0 total, peak 0 in a day. Stars: 0 total, peak 0 in a day.

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Stars, PRs, issues and forks are under-captured in the later part of this window. GH Archive progressively stopped capturing non-push events during 2026 — −95% or worse by the end of the window. Every series here except Pushes fades for that reason, so a decline above reflects the archive, not this repository. Pushes stay reliable throughout, so read them, and the contributor counts derived from them, as the real signal. Data health has the measurements.

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Pushes, PRs, issues, reviews and comments — stars and forks excluded, so this is contribution rather than popularity

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karandeeps182110

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  • Pull request#1karandeeps182025-04-02 10:42

Totals cover only the window loaded into ClickHouse and count events, not GitHub's lifetime totals — 0 stars here means stars gained during the window, not the repo's star count.