Bayesian deep neural networks in the proportional regime as renormalized kernel gaussian processes
Project description
Compute expected predictor of a Bayesian deep neural networks in the proportional regime in a non-parametric way using the equivalent GP
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
deepbays-0.1.4.tar.gz
(28.0 kB
view details)
Built Distribution
deepbays-0.1.4-py3-none-any.whl
(33.6 kB
view details)
File details
Details for the file deepbays-0.1.4.tar.gz
.
File metadata
- Download URL: deepbays-0.1.4.tar.gz
- Upload date:
- Size: 28.0 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.0.0 CPython/3.10.13
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 3dbcf203716c6948079f24be289ffec83dc9d49b7baac1b850c23cae6e7b8729 |
|
MD5 | 6200ac07201c346b63c6ebe2a3951ac5 |
|
BLAKE2b-256 | 81909ddddc6050f9fb43879655b36d6acd1b0aee8ae3df968a2aeb3ab9322eeb |
File details
Details for the file deepbays-0.1.4-py3-none-any.whl
.
File metadata
- Download URL: deepbays-0.1.4-py3-none-any.whl
- Upload date:
- Size: 33.6 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.0.0 CPython/3.10.13
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 77aa06d214dd417359676b1c0facf5ed7a1a0acf5af7fdba19328d303a5a86c0 |
|
MD5 | 84b8bcee7bf26c0d2e87bf1b6d4ae5d9 |
|
BLAKE2b-256 | 3208e7ff301b8c1e21598281075412e8f0fce3894b3e8b8d7bdfd075e8fd8cf8 |