https://github.com/janjagusch/pythonic-learning-machine

Python implementation of the Semantic Learning Machine
https://github.com/janjagusch/pythonic-learning-machine

artificial-intelligence genetic-algorithm genetic-programming machine-learning neat-python neural-network neuro-evolution python pythonic-learning-machine semantic-learning-machine

Last synced: 11 days ago
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Python implementation of the Semantic Learning Machine

• Host: GitHub
• URL: https://github.com/janjagusch/pythonic-learning-machine
• Owner: janjagusch
• License: mit
• Created: 2018-03-03T16:20:57.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2021-06-27T18:25:45.000Z (over 3 years ago)
• Last Synced: 2023-03-06T18:01:12.566Z (over 1 year ago)
• Topics: artificial-intelligence, genetic-algorithm, genetic-programming, machine-learning, neat-python, neural-network, neuro-evolution, python, pythonic-learning-machine, semantic-learning-machine
• Language: HTML
• Size: 12.2 MB
• Stars: 2
• Watchers: 2
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
pythonic-learning-machine

==============================

Python implementation of the Semantic Learning Machine.

Project Organization

------------

    ├── LICENSE

    ├── Makefile           <- Makefile with commands like `make data` or `make train`

    ├── README.md          <- The top-level README for developers using this project.

    ├── data

    │   ├── external       <- Data from third party sources.

    │   ├── interim        <- Intermediate data that has been transformed.

    │   ├── processed      <- The final, canonical data sets for modeling.

    │   └── raw            <- The original, immutable data dump.

    │

    ├── docs               <- A default Sphinx project; see sphinx-doc.org for details

    │

    ├── models             <- Trained and serialized models, model predictions, or model summaries

    │

    ├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),

    │                         the creator's initials, and a short `-` delimited description, e.g.

    │                         `1.0-jqp-initial-data-exploration`.

    │

    ├── references         <- Data dictionaries, manuals, and all other explanatory materials.

    │

    ├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.

    │   └── figures        <- Generated graphics and figures to be used in reporting

    │

    ├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.

    │                         generated with `pip freeze > requirements.txt`

    │

    ├── src                <- Source code for use in this project.

    │   ├── __init__.py    <- Makes src a Python module

    │   │

    │   ├── data           <- Scripts to download or generate data

    │   │   └── make_dataset.py

    │   │

    │   ├── features       <- Scripts to turn raw data into features for modeling

    │   │   └── build_features.py

    │   │

    │   ├── models         <- Scripts to train models and then use trained models to make

    │   │   │                 predictions

    │   │   ├── predict_model.py

    │   │   └── train_model.py

    │   │

    │   └── visualization  <- Scripts to create exploratory and results oriented visualizations

    │       └── visualize.py

    │

    └── tox.ini            <- tox file with settings for running tox; see tox.testrun.org

--------

Project based on the cookiecutter data science project template. #cookiecutterdatascience