Skip to main content

Auto-Differentiation Package based on Forward Mode

Project description

cs107-FinalProject

Final Project submission

Group 18

Members: Ethan Schumann, Alice Li, Ruizhe Kang, Vikram Shastry

codecov

Build Status

Broader Impact

This tool to perform automatic differentiation serves to help automate the calculation of derivatives and has potential to become more preferable to traditional calculation methods. As software developers and researchers, it is our duty to consider the possible positive and negative outcomes of sharing our software openly. Although the applications of derivative calculation tasks are vast, we can consider a few examples where certain applications may impact the society and day to day life. The maximization of functions in gradient descent methods require derivatives and the application of machine learning may be applied to real life tasks such as self-driving or the replacement of roles occupied traditionally by humans such as court judges. In both cases, the decisions of machine learning programs can have severe consequences for human lives. In self driving, the ethical implications could consider whether or not to prioritize the lives of passengers versus pedestrians in an accident. In court cases, the ethical implications involve whether or not to remove human biases by replacing human judges.

Software Inclusivity

Equally important is the need to address inclusivity for all software developers whether open source or in the workplace. There should not be any barriers at all to prevent other developers from contributing to our software package. Despite the availability of resources to freely share code like GitHub, recent surveys have shown that of the few (3%) women who responded, the majority (68%) wanted to contribute to open source but were less likely overall to do so. The lack of resources for younger developers and women in open source highlights the need for the more traditional male developers in open source to share a more open view towards diversity and mentorship. These barriers towards diversity are damaging to the tech industry as open-source projects are increasingly valued as evidence of skill for job hires. This package's development team demonstrated inclusivity in the course of this project. The development team consisted of persons of different genders, ages, ethnicities, family backgrounds, software experience, and educational backgrounds. The team included both full-time students and a full-time working professional.

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

AD_Derivators-0.0.2.tar.gz (16.2 kB view details)

Uploaded Source

Built Distribution

AD_Derivators-0.0.2-py3-none-any.whl (19.3 kB view details)

Uploaded Python 3

File details

Details for the file AD_Derivators-0.0.2.tar.gz.

File metadata

  • Download URL: AD_Derivators-0.0.2.tar.gz
  • Upload date:
  • Size: 16.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.6.1 requests/2.25.0 setuptools/49.2.1 requests-toolbelt/0.9.1 tqdm/4.54.1 CPython/3.8.6

File hashes

Hashes for AD_Derivators-0.0.2.tar.gz
Algorithm Hash digest
SHA256 c18a5926fb5bd62f7480e6b5ff926785ab03cd8f67f8f9d2d4d61fa694a79f29
MD5 dcdbdee1501327b0b58ce00dc83a9aff
BLAKE2b-256 d76ac8a6beecf73a03ae834ffa1a1c57ea73bedc7d30762b75d1a38e5a2385a1

See more details on using hashes here.

File details

Details for the file AD_Derivators-0.0.2-py3-none-any.whl.

File metadata

  • Download URL: AD_Derivators-0.0.2-py3-none-any.whl
  • Upload date:
  • Size: 19.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.6.1 requests/2.25.0 setuptools/49.2.1 requests-toolbelt/0.9.1 tqdm/4.54.1 CPython/3.8.6

File hashes

Hashes for AD_Derivators-0.0.2-py3-none-any.whl
Algorithm Hash digest
SHA256 f9db7c6e623f2fd90371bcd7f73c68ad0148f37724d2ed4d3a078ea9a4ed2bb5
MD5 a9700e5e55094b95e9bd3cbc2d3c9cb9
BLAKE2b-256 27a3f131202fb70c091dfd591f9555d6f8b62c9e92eee8b7aa2f4dda39ba5150

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