vowpal-wabbit 8.6.1.dfsg1-1build2 source package in Ubuntu
Changelog
vowpal-wabbit (8.6.1.dfsg1-1build2) focal; urgency=medium * No-change rebuild for libgcc-s1 package name change. -- Matthias Klose <email address hidden> Mon, 23 Mar 2020 08:58:25 +0100
Upload details
- Uploaded by:
- Matthias Klose
- Uploaded to:
- Focal
- Original maintainer:
- Ubuntu Developers
- Architectures:
- any all
- Section:
- science
- Urgency:
- Medium Urgency
See full publishing history Publishing
| Series | Published | Component | Section | |
|---|---|---|---|---|
| Focal | release | universe | science |
Downloads
| File | Size | SHA-256 Checksum |
|---|---|---|
| vowpal-wabbit_8.6.1.dfsg1.orig.tar.gz | 17.1 MiB | 83c7ce87df09ba18413e4b5bfbb6e8c9210b81a2762de96e84c924b94e9e28e4 |
| vowpal-wabbit_8.6.1.dfsg1-1build2.debian.tar.xz | 6.8 KiB | bbd467f0d2bff67f03c1086a39c66e5d55638fa2bf16ac56b89191f932b7509a |
| vowpal-wabbit_8.6.1.dfsg1-1build2.dsc | 2.3 KiB | c80e92a504175b4b15b0cf32c00ba25480be52f32d89205284e63b9b4e9cffac |
Available diffs
Binary packages built by this source
- libvw-dev: No summary available for libvw-dev in ubuntu groovy.
No description available for libvw-dev in ubuntu groovy.
- libvw0: fast and scalable online machine learning algorithm - dynamic library
Vowpal Wabbit is a fast online machine learning algorithm. The core
algorithm is specialist gradient descent (GD) on a loss function
(several are available). VW features:
- flexible input data specification
- speedy learning
- scalability (bounded memory footprint, suitable for distributed
computation)
- feature pairing
.
This package contains vowpal-wabbit's dynamic libraries.
- vowpal-wabbit: No summary available for vowpal-wabbit in ubuntu hirsute.
No description available for vowpal-wabbit in ubuntu hirsute.
- vowpal-wabbit-dbg: No summary available for vowpal-wabbit-dbg in ubuntu groovy.
No description available for vowpal-wabbit-dbg in ubuntu groovy.
- vowpal-wabbit-doc: fast and scalable online machine learning algorithm - documentation
Vowpal Wabbit is a fast online machine learning algorithm. The core
algorithm is specialist gradient descent (GD) on a loss function
(several are available). VW features:
- flexible input data specification
- speedy learning
- scalability (bounded memory footprint, suitable for distributed
computation)
- feature pairing
.
This package contains examples (tests) for vowpal-wabbit.
