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accord-net
framework
C#2016–2021
accord-net / framework · Archived

framework

Machine learning, computer vision, statistics and general scientific computing for .NET

AI & Machine LearningSystems & InfrastructureCreative & MediaScience & EngineeringLearning & ReferenceNetworking
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5Distinct people
5Mentions
0Explicit recommenders
4.5KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Machine learning, computer vision, statistics and general scientific computing for .NET

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 1:49:46 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year5 dated mentions

Observed mentions: 2016-03-27 15:38:14 UTC to 2021-06-03 21:29:47 UTC.

01232016-01-01 00:00:00 UTC to 2017-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments20162017-01-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 3 mentions; 1 submissions, 2 comments20182019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2021

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20162016-01-01 00:00:00 UTC2017-01-01 00:00:00 UTC101
20172017-01-01 00:00:00 UTC2018-01-01 00:00:00 UTC000
20182018-01-01 00:00:00 UTC2019-01-01 00:00:00 UTC312
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC000
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC000
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC101

What Hacker News says

5 sources
Rapzid·Dec 22, 2018
Hmm, didn't realize that; I only ever really verified it worked and worked with F# haha. In the scheme of things the optimizers are rather small bits that could be encapsulated quickly.. Poking around it looks like Accord.Net is interested in getting CNN support in via TensorFlowSharp: https://github.com/accord-net/framework/issues/92 . Then there is ConvNetSharp. I'm sure a fun project for someone would also be to implement all this in a pure F# library. It supposedly has some pretty good GPU libs.
In Machine Learning for .NET
allisterb·Oct 31, 2018Submission

Show HN: Cyber-forensics for public PCs using ML to detect hate speech, threats

This is a digital forensics project I've been working on called OLAF which is designed to provide forensic evidence in investigations of using library or other public-facing PCs to send hate speech, violent threats, "swatting", IP exfiltration or infiltration, or to commit any other type of computer-enabled crimes or malfeasance. The project uses OCR, NLP, and computer vision to create high level logs of the PC's interactive usage that can be used in investigation or detection of dangerous or criminal activity. Demo vid 1: https://youtu.be/F6k2PC-7WQw Demo vid 2: https://youtu.be/-N2Qftl26Mw Project repository: https://github.com/allisterb/OLAF The raw data OLAF uses is logged in disparate places in one form or another (like in your PC or ISP or web server logs) and OLAF just tries to do real-time analysis of the available data in one p
Read the rest
lace. No PII is ever intentionally logged and it is up to the org to link PC user accounts and identities to people. Of course privacy is a big concern here and the organizations using a tool like OLAF have to walk the line between protecting the privacy of their users and adhering to the relevant laws vs. being able to quickly detect and investigate potentially serious threats to people's safety. Guidelines like those from NISO (https://www.niso.org/publications/privacy-principles) should be adhered to as much as possible. Libraries currently used: Tesseract.net: https://github.com/tvncosine/tesseract.net VADERSharp: https://github.com/codingupastorm/vadersharp Accord.NET: https://github.com/Accord-net/framework/ Azure Cognitive Services Computer Vision API Azure Cognitive Services Text Analytics API Let me know what you guys think and any comments or suggestions (or criticism) you may have.
4 points · Open discussion

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