Science Score: 44.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
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○Academic publication links
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○Academic email domains
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (8.4%) to scientific vocabulary
Keywords
Repository
Second-Order Differential ML
Basic Info
- Host: GitHub
- Owner: neilkichler
- License: mit
- Language: Jupyter Notebook
- Default Branch: main
- Homepage: https://neilkichler.github.io/master-thesis
- Size: 285 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Topics
Metadata Files
README.md
Second-Order Differential Machine Learning
For detailed background information, consider looking into:
https://neilkichler.github.io/master-thesis/Thesis.pdf
Notebooks
The notebooks contain the majority of the code right now and accumulate the ideas that are needed for the proposed methods. The package currently implements the core functions needed for Sobolev Training / Differential Machine Learning.
Installation
Clone the repo and execute the following inside the root folder.
bash
python -m pip install -e .
Development
We use Hatch as the project manager. The usual commands apply.
Show all available scripts
bash
hatch env show
Run case study
bash
hatch -e example run python examples/bachelier/bachelier.py
Run Tests
bash
hatch run test:test
Build project wheel
bash
hatch build
Lint project
bash
hatch run lint:fmt
Owner
- Name: Neil Kichler
- Login: neilkichler
- Kind: user
- Repositories: 1
- Profile: https://github.com/neilkichler
Citation (CITATION.cff)
cff-version: 1.2.0
title: Second-Order Differential Machine Learning using Jax
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Neil
family-names: Kichler
email: neil.kichler@rwth-aachen.de
affiliation: RWTH Aachen
repository-code: 'https://github.com/neilkichler/diff-ml'
abstract: TODO
keywords:
- jax
- differential-machine-learning
- neural-networks
license: MIT
commit: 34b7e5d40e8a8f64841d126a7c8bd723cea0705e
version: 0.0.1
date-released: '2023-07-25'
GitHub Events
Total
Last Year
Dependencies
- actions/cache v4 composite
- actions/checkout v4 composite
- actions/setup-python v5 composite
- chex ==0.1.5
- equinox ==0.9.2
- jax ==0.3.25
- matplotlib >=3.7.1
- optax ==0.1.4
- matplotlib >=3.7.1
- tensorflow ==2.10.1
- tensorflow-probability ==0.18.0
- equinox >=0.10.5
- jax >=0.4.16
- jaxlib >=0.4.16
- jaxtyping >=0.2.20
- optax >=0.1.7
- tqdm >=4.66.4