A simple numpy based neural network library inspired by Tensorflow/Keras.
Project description
bren is a custom numpy based library, powered by automatic differentiation, inspired by Tensorflow/Keras, which allows users to build small scale simple neural networks. It's analogous yet simpler design to the Keras api allows users to produce, train and save their own models, with custom components, without having to learn an entirely new structure.
bren is part of a sequence of neural network from scratch projects and a successor to the neural-network-from-scratch-v2, with one major update being the integration of automatic differentitation. Automatic differentiation allows for the real-time determination of derivatives during back propagation and reduces the need for users to couple mathematical computation with derivatives as was required in the previous projects.
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
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file bren-0.1.6.tar.gz.
File metadata
- Download URL: bren-0.1.6.tar.gz
- Upload date:
- Size: 31.8 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.11.4
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
ca7d2e430e4d5205e5d75b9dbcde20fe6db4560faaa136de7f1c38d9db72b006
|
|
| MD5 |
5aadbe5980007458b2a22bfae9adad32
|
|
| BLAKE2b-256 |
31dbf4e7a823a62977420f13fd16370484dc9867792bfbf01255bd2b9683babc
|
File details
Details for the file bren-0.1.6-py3-none-any.whl.
File metadata
- Download URL: bren-0.1.6-py3-none-any.whl
- Upload date:
- Size: 44.8 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.11.4
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
ae29a57068bd2c82ac698d9646622c18c6e873e3a6bb70ce11afd7b548efd966
|
|
| MD5 |
2599aaa10c73d5f584f2b45d6450d262
|
|
| BLAKE2b-256 |
16a52550c881eb24466b9d987c122631d547e83fe5ccd0d82860deb3827427dd
|