AD2020 is a Python package for automatic differentiation (AD). Differentiation is one of the key components in numerical computing and is ubiquitous in machine learning, optimization, and numerical methods. By repeatedly applying chain rule to elementary arithmetic operations and functions, AD is powerful to efficiently compute derivatives for complicated functions to machine precision.
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Release files for AD2020 0.0.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| AD2020-0.0.2.tar.gz | 8.6 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| AD2020-0.0.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 19.6 kB
Release files / AD2020-0.0.2.tar.gz
| Download URL | AD2020-0.0.2.tar.gz |
|---|---|
| Size | 8.6 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/51.0.0 requests-toolbelt/0.9.1 tqdm/4.47.0 CPython/3.8.3
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Release files / AD2020-0.0.2-py3-none-any.whl
| Download URL | AD2020-0.0.2-py3-none-any.whl |
|---|---|
| Size | 11.0 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/51.0.0 requests-toolbelt/0.9.1 tqdm/4.47.0 CPython/3.8.3
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Security for AD2020 0.0.2
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