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deeplearning4j
datavec
HISTORICAL RECORD2016–2018
deeplearning4j / datavec · Not publicly available

datavec

Discover this repository through its Hacker News conversations.

2Distinct people
6Mentions
0Explicit recommenders
REPOSITORY AT A GLANCE

What to know before exploring

What it is

A public description has not been verified.

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

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 7:02:50 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 month6 dated mentions

Observed mentions: 2016-12-31 15:44:54 UTC to 2018-01-17 04:31:35 UTC.

012016-12-01 00:00:00 UTC to 2017-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsDec 20162017-01-01 00:00:00 UTC to 2017-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-02-01 00:00:00 UTC to 2017-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-03-01 00:00:00 UTC to 2017-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-04-01 00:00:00 UTC to 2017-05-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2017-05-01 00:00:00 UTC to 2017-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-06-01 00:00:00 UTC to 2017-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsJun 20172017-07-01 00:00:00 UTC to 2017-08-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2017-08-01 00:00:00 UTC to 2017-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-09-01 00:00:00 UTC to 2017-10-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2017-10-01 00:00:00 UTC to 2017-11-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2017-11-01 00:00:00 UTC to 2017-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-12-01 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 2018-02-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsJan 2018

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
Dec 20162016-12-01 00:00:00 UTC2017-01-01 00:00:00 UTC101
Jan 20172017-01-01 00:00:00 UTC2017-02-01 00:00:00 UTC000
Feb 20172017-02-01 00:00:00 UTC2017-03-01 00:00:00 UTC000
Mar 20172017-03-01 00:00:00 UTC2017-04-01 00:00:00 UTC000
Apr 20172017-04-01 00:00:00 UTC2017-05-01 00:00:00 UTC101
May 20172017-05-01 00:00:00 UTC2017-06-01 00:00:00 UTC000
Jun 20172017-06-01 00:00:00 UTC2017-07-01 00:00:00 UTC000
Jul 20172017-07-01 00:00:00 UTC2017-08-01 00:00:00 UTC101
Aug 20172017-08-01 00:00:00 UTC2017-09-01 00:00:00 UTC000
Sep 20172017-09-01 00:00:00 UTC2017-10-01 00:00:00 UTC101
Oct 20172017-10-01 00:00:00 UTC2017-11-01 00:00:00 UTC101
Nov 20172017-11-01 00:00:00 UTC2017-12-01 00:00:00 UTC000
Dec 20172017-12-01 00:00:00 UTC2018-01-01 00:00:00 UTC000
Jan 20182018-01-01 00:00:00 UTC2018-02-01 00:00:00 UTC101

What Hacker News says

1 sources
The core looks close enough to dataframes that I'd be curious to know how you compare to tablesaw: https://github.com/jtablesaw/tablesaw This looks neat but I'm not sure why I would care about this. There's a ton of solutions out there in the ecosystem out there already with a columnar like interface. Granted, we wrote our own as well[1] that uses the builder pattern that you then toss to an executor (our main backend is spark for this). One reason we wrote this is for persistence purposes. Being able to encode and persist a series of transforms that you can then load remotely has been very helpful for us in machine learning. We've since migrated this project to the eclipse foundation and intend on doing a rewrite of the interface as well as integrate our baked in tensor library[2] in to certain parts of the pipeline for speed purposes an
Read the rest
d handling things like computer vision workloads. In general, I always like seeing new takes on the columnar format processing approach but I'm just not seeing anything novel here. Clarification of intent would be great! [1]: https://github.com/deeplearning4j/DataVec [2]: https://github.com/deeplearning4j/nd4j
In Show HN: Bistro – A light-weight column-oriented data processing engine

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