Wheelodex » Projects » autograd

autograd

View on PyPI — Reverse Dependencies (124)

1.7.0 autograd-1.7.0-py3-none-any.whl

Wheel Details

Project: autograd
Version: 1.7.0
Filename: autograd-1.7.0-py3-none-any.whl
Download: [link]
Size: 52522
MD5: 063819f32a38b51d0bd742c23945a20f
SHA256: 49680300f842f3a8722b060ac0d3ed7aca071d1ad4d3d38c9fdadafdcc73c30b
Uploaded: 2024-08-22 19:07:12 +0000

dist-info

METADATA · WHEEL · RECORD

METADATA

Metadata-Version: 2.3
Name: autograd
Version: 1.7.0
Summary: Efficiently computes derivatives of NumPy code.
Author-Email: Dougal Maclaurin <maclaurin[at]physics.harvard.edu>, David Duvenaud <duvenaud[at]cs.toronto.edu>, Matthew Johnson <mattjj[at]csail.mit.edu>, Jamie Townsend <j.h.n.townsend[at]uva.nl>
Maintainer-Email: Jamie Townsend <j.h.n.townsend[at]uva.nl>, Fabian Joswig <fabian.joswig[at]uni-muenster.de>, Agriya Khetarpal <agriyakhetarpal[at]outlook.com>
Project-Url: Source, https://github.com/HIPS/autograd
License: The MIT License (MIT) Copyright (c) 2014 by the President and Fellows of Harvard University Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Keywords: Automatic differentiation,NumPy,Python,SciPy,backpropagation,gradients,machine learning,neural networks,optimization
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.8
Requires-Dist: numpy
Requires-Dist: scipy; extra == "scipy"
Provides-Extra: scipy
Description-Content-Type: text/markdown
[Description omitted; length: 4933 characters]

WHEEL

Wheel-Version: 1.0
Generator: hatchling 1.25.0
Root-Is-Purelib: true
Tag: py3-none-any

RECORD

Path Digest Size
autograd/__init__.py sha256=BBBJgTQopesU1Oa_ZvampGOYlwYBnZZntp2mYQ1uurw 504
autograd/builtins.py sha256=UVBcTXH8bYltSYLk56vG7gX1rlbko5mJxdGixe9wRBM 6260
autograd/core.py sha256=9VfC2I0sEOLNMmxHGtS4wSeyQG3upxN7Zr9k_PqxN-8 12235
autograd/differential_operators.py sha256=x6CQuPrP2JOphCZGzycKJj6lpZAN5AZK9pmopDtfIzw 8515
autograd/extend.py sha256=HOlBeBM566zcc83pCz58fYMHA2b3IANCFd9KC8W1VDk 304
autograd/test_util.py sha256=P_n6-KeYynu4jaSo4jQCQCjLzBv_nPtWJH3MIJ3N2jU 2881
autograd/tracer.py sha256=usP92zCxez9HRINTCXkTUr1-tHlJAW6rC2QTGa4iBG8 3951
autograd/util.py sha256=JJ4ORHAPilsTlpbDiiW8sdKWUqtbkw4XPtOP_4qH7jU 1482
autograd/wrap_util.py sha256=psBcdd87AyPszDs4YYGhRctzuniSdtlYXZMVdmd_lkM 1581
autograd/misc/__init__.py sha256=ldlSqSKWpOsSu5iDj3Jn_rZ8sRkhCHajbr0Lfd1WmDg 62
autograd/misc/fixed_points.py sha256=51oXRnneU9JpQEHqCX2MwaZFG7OcccqPfKTqLMudQCk 771
autograd/misc/flatten.py sha256=OLHsazd2S0vt2DAG9vftsKsWHbIWXcdYmlvR1ZQhTy0 1120
autograd/misc/optimizers.py sha256=d_5GxwMSDCb_3RHatTsUJZiP-ZEPwl9WxKzPJhhVHxI 2757
autograd/misc/tracers.py sha256=cVwxWbYK6wV6hqSlI2H5dqo6soqLvEbizprI8uZG9YE 2200
autograd/numpy/__init__.py sha256=C2tQAXcIcRkTZg8TeTpIYK893_yBi_w2fqgI4GTOlOs 232
autograd/numpy/fft.py sha256=X0BMaiNvf1KdhhDHHOTOoUiILDg5m1B7KSS3OUflnZw 5250
autograd/numpy/linalg.py sha256=xgWuE1d0OKaQb6VR3uuYEBxhvzaoPPtxwrvOSSnt6Wk 11352
autograd/numpy/numpy_boxes.py sha256=MwNGGQwlH6SM8P_uF-Gc2qo3O9SjwbGacWogpQj8oM4 3823
autograd/numpy/numpy_jvps.py sha256=NJ5Vpl2eKCNzsLA25Uhumi8DQXHXF7Otv9x-yEpGvBc 10842
autograd/numpy/numpy_vjps.py sha256=_tExFsMC3JkwvlkdWa7cBoyzteFb97wV-SEjGsbyIL0 32575
autograd/numpy/numpy_vspaces.py sha256=tRv2WpOl4tPz1Z_4rEe1IuPcveTXV6dQUeYl0MOaGjA 3632
autograd/numpy/numpy_wrapper.py sha256=ndji8vCPdHRIwwbBOXI7_q4t2oYRCKzVWfT7FdRcPIg 5459
autograd/numpy/random.py sha256=lEO11VkNZV8_nXhZj0QGxMW0u62N1JsZKwS_r4FS-WY 149
autograd/scipy/__init__.py sha256=UKZXpo72Jo5-AyZ7rgMqEXqlWDNKwxDPi2mcx3bpXs0 183
autograd/scipy/integrate.py sha256=0iQwPdsfMvsiXTc432GMqIZsHinRTtEG2dGL9ol9Gmc 2867
autograd/scipy/linalg.py sha256=ykzxZVLp9aGV6unl5K0rMzINaaDw2p0VVZx-MndaQqU 4240
autograd/scipy/misc.py sha256=s-lvhZCHuCu0L52FsxWfNXUJDYs5Mw7VVQ4wDj2zY8U 168
autograd/scipy/signal.py sha256=yiWaqE3Ud3b5OwTvxKtZtUIaFkDY5HmP2r-YUN6FIPg 5886
autograd/scipy/special.py sha256=lgIhf8OIBIPfu8eY7Cu3BkN4gaPBwAZ6_FZTiG5DYDg 5135
autograd/scipy/stats/__init__.py sha256=0LMQOVf8Pi89EM8ntp33pbFhAlbVJlLy58nubsGgKnI 391
autograd/scipy/stats/beta.py sha256=WEbfPsYoaDbgxeiHz700tECqcJZSr1icFWxWWK2P0KQ 1332
autograd/scipy/stats/chi2.py sha256=DTPB_kkmD3_wsQsa9t6kBf7tObgTLrNBd_WzGhm-rEo 809
autograd/scipy/stats/dirichlet.py sha256=AzCqAJoVc4d3MUEEDwaBh2eo1I2_1HmLaoepbreoMU4 772
autograd/scipy/stats/gamma.py sha256=CzJB3OmuLjCMTzEio-zpsM83hcAmppyStDHG45YsyEc 987
autograd/scipy/stats/multivariate_normal.py sha256=qIWGSRYuTvJScgOpHnFz1y3pZFqzMZkVJNDgmJmzCNU 2509
autograd/scipy/stats/norm.py sha256=nNH7y7lR3jC8mbSOHgrAADrxugYKua7cqWKEs-4rcQI 2758
autograd/scipy/stats/poisson.py sha256=hiptL8OCnCDQIvUwO59E_yYIL0x98YtpBufLz8TNICs 715
autograd/scipy/stats/t.py sha256=XZvuscTUrt3PQBPs8An0OAT5wcMWh5Ke4mXkHbi222Q 2670
autograd-1.7.0.dist-info/METADATA sha256=bJR26CAFuCDmQMJlzKv5_FYUUz7aIy2cdJMEWecGI-U 7489
autograd-1.7.0.dist-info/WHEEL sha256=1yFddiXMmvYK7QYTqtRNtX66WJ0Mz8PYEiEUoOUUxRY 87
autograd-1.7.0.dist-info/RECORD — —