ANN and Machine Learning Examples Package
This Python package provides various implementations of fundamental machine learning algorithms, including perceptron learning, neural networks, and naive Bayes, among others. Each function is designed to illustrate core ML concepts in an educational and practical way. This package can serve as a learning tool for students, educators, and developers interested in experimenting with machine learning algorithms and neural networks.
Table of Contents
Installation
You can install the package from PyPI by running:
pip install annuvce
Release files for annuvce 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| annuvce-0.1.0.tar.gz | 6.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| annuvce-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 13.6 kB
Release files / annuvce-0.1.0.tar.gz
| Download URL | annuvce-0.1.0.tar.gz |
|---|---|
| Size | 6.7 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/5.1.1 CPython/3.12.6
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Release files / annuvce-0.1.0-py3-none-any.whl
| Download URL | annuvce-0.1.0-py3-none-any.whl |
|---|---|
| Size | 6.8 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.1.1 CPython/3.12.6
|