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CalculatedContent
WeightWatcher
Python2020–2026
CalculatedContent / WeightWatcher

WeightWatcher

The WeightWatcher tool for predicting the accuracy of Deep Neural Networks

4Distinct people
5Mentions
0Explicit recommenders
1.8KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

The WeightWatcher tool for predicting the accuracy of Deep Neural Networks

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 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 8:03:49 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: 2020-03-22 03:33:48 UTC to 2026-02-04 17:15:35 UTC.

012020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments20202021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments20232024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments2026-01-01 00:00:00 UTC to 2027-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments. Incomplete calendar period.2026*

* An incomplete calendar period is marked with an outlined bar. Its count is not directly comparable with a full period.

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
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC101
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC101
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC000
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC101
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC000
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC110
2026 (partial)2026-01-01 00:00:00 UTC2027-01-01 00:00:00 UTC101

What Hacker News says

1 sources
Here's an alternative approach, that actually provides real world results https://calculatedcontent.com/2019/12/03/towards-a-new-theor... Using techniques from statistical mechanics and strongly correlated systems, we can compute the average-case-behavior of a real world DNN We believe we can reproduce some of the results of the NTK by using a Gaussian Random Matrix. But if we use a more realistic, heavy tailed matrix, we get more practical results A early fork of the theory has been published in JMLR https://arxiv.org/abs/1810.01075 and the empirical results in Nature Communications https://www.nature.com/articles/s41467-021-24025-8 and we have an open source tool , weightwatcher, which can be used in production pip install weightwatcher https://github.com/CalculatedContent/WeightWatcher Please give it a try and let me know if it is us
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In A new link to an old model could crack the mystery of deep learning

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