Skip to main content

A flexible machine learning framework

Project description

The Cottonwood Machine Learning Framework

Cottonwood is built to as flexible as possible, top to bottom. It's designed to minimize the iteration time when running experiments and testing ideas. It's meant to be tweaked. Fork it. Add to it. Customize it to solve the problem at hand. For more of the thought behind it, read the post " Why another framework?

This code is always evolving. I recommend referencing a specific tag whenever you use it in a project. Tags are labeled v1, v2, etc. and the code attached to each one won't change.

If you want to follow along with the construction process for Cottonwood, you can get a step-by-step walkthrough in End-to-End Machine Learning Course 312 and Course 313

Try it out

python3 -m pip install "cottonwood==7"
python3
>>> import cottonwood.demo

Revision history

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

cottonwood-7.tar.gz (15.5 kB view details)

Uploaded Source

Built Distribution

cottonwood-7-py3-none-any.whl (47.7 kB view details)

Uploaded Python 3

File details

Details for the file cottonwood-7.tar.gz.

File metadata

  • Download URL: cottonwood-7.tar.gz
  • Upload date:
  • Size: 15.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.6.0 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.6.8

File hashes

Hashes for cottonwood-7.tar.gz
Algorithm Hash digest
SHA256 dc00bdbbd8f87192986c7c128373aa914ffc74269795b8692aa748c4687b5e93
MD5 d327811abfe37f092f7af2a4fff36ee7
BLAKE2b-256 2135acf5a7f9ef381c8bf236d41c0a2301b703d179d5691a08c5ae9cb9e30e2d

See more details on using hashes here.

File details

Details for the file cottonwood-7-py3-none-any.whl.

File metadata

  • Download URL: cottonwood-7-py3-none-any.whl
  • Upload date:
  • Size: 47.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.6.0 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.6.8

File hashes

Hashes for cottonwood-7-py3-none-any.whl
Algorithm Hash digest
SHA256 0b3e3a397e3ef8e0c087b16af4803b3f148732a05e4d17da42cbddc3e0cf352b
MD5 642fced2909058f6e9c65f0762fdf1d1
BLAKE2b-256 2419103cc776c9f01ec7034580103dcbe2ca504666b3ec19a802eac4e624bc45

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page