{"id":18075655,"url":"https://github.com/lechemi/machine-learning-vademecum","last_synced_at":"2025-06-20T12:35:49.141Z","repository":{"id":258536439,"uuid":"862373397","full_name":"Lechemi/machine-learning-vademecum","owner":"Lechemi","description":"Un notebook contenente nozioni di base ed esempi pratici in python sul machine learning.","archived":false,"fork":false,"pushed_at":"2024-10-22T07:12:38.000Z","size":582,"stargazers_count":3,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-04-02T06:43:14.595Z","etag":null,"topics":["machine-learning","python","scikit-learn"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Lechemi.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-09-24T13:46:51.000Z","updated_at":"2025-03-14T13:06:35.000Z","dependencies_parsed_at":"2024-10-22T13:14:09.649Z","dependency_job_id":null,"html_url":"https://github.com/Lechemi/machine-learning-vademecum","commit_stats":null,"previous_names":["lechemi/machine-learning-vademecum"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Lechemi/machine-learning-vademecum","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Lechemi%2Fmachine-learning-vademecum","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Lechemi%2Fmachine-learning-vademecum/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Lechemi%2Fmachine-learning-vademecum/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Lechemi%2Fmachine-learning-vademecum/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Lechemi","download_url":"https://codeload.github.com/Lechemi/machine-learning-vademecum/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Lechemi%2Fmachine-learning-vademecum/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":260945650,"owners_count":23087052,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["machine-learning","python","scikit-learn"],"created_at":"2024-10-31T11:06:47.749Z","updated_at":"2025-06-20T12:35:44.121Z","avatar_url":"https://github.com/Lechemi.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Guida all'utilizzo del notebook\nPuoi eseguire il comando\n  ```console\n  pip install -r requirements.txt\n  ```\nper installare i moduli necessari.  \nPer ottenere il notebook a partire dal file `ml_vademecum.py`, vedi il paragrafo 'Conversioni' di seguito.\n\n### Perché utilizziamo Jupytext?\nJupytext permette, tra le altre cose, di convertire qualsiasi notebook Jupyter in un file `.py` costituito solo dal contenuto effettivo del notebook e privo di tutti i metadati che i file `.ipynb` si trascinano dietro. In questo modo, versioniamo solo ciò che è strettamente necessario.\n\n## Utilizzo di Jupytext\n### Conversioni\n- Da `.ipynb` a  `.py` :\n  \n  ```console\n  jupytext --to py notebook.ipynb \n  ```\n- Da `.py` a  `.ipynb` :\n  \n  ```console\n  jupytext --to notebook notebook.py\n  ```\n### Sincronizzazione\nJupytext permette di creare una coppia sincronizzata di notebook ipynb/py, e offre una modalità `--sync` che aggiorna automaticamente il file meno recente della coppia.  \nPer convertire `notebook.ipynb` in una coppia sincronizzata di notebook ipynb/py:\n```console\njupytext --set-formats ipynb,py notebook.ipynb\n```\nPer aggiornare il file meno recente della coppia (non importa quale dei due viene specificato):\n```console\njupytext --sync notebook.ipynb\n```\n\n[Qui](https://jupytext.readthedocs.io/en/latest/using-cli.html) si trova una serie di comandi utili, tra cui quelli presentati.\n\n##  Licenza\nQuesto progetto utilizza materiali sotto la licenza MIT.\n\nCopyright (c) 2019 Amirsina Torfi\nLicensed under the MIT License.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flechemi%2Fmachine-learning-vademecum","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flechemi%2Fmachine-learning-vademecum","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flechemi%2Fmachine-learning-vademecum/lists"}