{"id":19068116,"url":"https://github.com/kweinmeister/notebooks","last_synced_at":"2025-06-23T17:35:28.305Z","repository":{"id":196636750,"uuid":"172573629","full_name":"kweinmeister/notebooks","owner":"kweinmeister","description":"Jupyter notebooks for learning and demonstrations","archived":false,"fork":false,"pushed_at":"2025-06-03T20:40:29.000Z","size":522,"stargazers_count":11,"open_issues_count":0,"forks_count":4,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-06-04T07:47:02.504Z","etag":null,"topics":["machine-learning","notebook","python","tensorflow"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/kweinmeister.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","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,"zenodo":null}},"created_at":"2019-02-25T19:48:34.000Z","updated_at":"2025-06-03T20:40:30.000Z","dependencies_parsed_at":null,"dependency_job_id":"5e713861-8708-4e80-9ab4-c283472b289a","html_url":"https://github.com/kweinmeister/notebooks","commit_stats":null,"previous_names":["kweinmeister/notebooks"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/kweinmeister/notebooks","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kweinmeister%2Fnotebooks","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kweinmeister%2Fnotebooks/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kweinmeister%2Fnotebooks/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kweinmeister%2Fnotebooks/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/kweinmeister","download_url":"https://codeload.github.com/kweinmeister/notebooks/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kweinmeister%2Fnotebooks/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":261523107,"owners_count":23171959,"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","notebook","python","tensorflow"],"created_at":"2024-11-09T01:04:57.155Z","updated_at":"2025-06-23T17:35:28.294Z","avatar_url":"https://github.com/kweinmeister.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"**This is not an official Google product.**\n\n# Notebooks\n\n- [Notebooks](#notebooks)\n  - [Google Drive ZIP to GitHub Repository Exporter](#google-drive-zip-to-github-repository-exporter)\n  - [Identifying LLM \"Tells\": N-gram Analysis of Human vs. AI Text](#identifying-llm-tells-n-gram-analysis-of-human-vs-ai-text)\n  - [Querying a GitHub Codebase with Vertex AI RAG Engine](#querying-a-github-codebase-with-vertex-ai-rag-engine)\n  - [Product Data Enrichment with Vertex AI](#product-data-enrichment-with-vertex-ai)\n  - [Causal Inference with Vertex AI AutoML Forecasting](#causal-inference-with-vertex-ai-automl-forecasting)\n  - [Medical Imaging notebooks using Vertex AI](#medical-imaging-notebooks-using-vertex-ai)\n  - [Understand how your TensorFlow model is making predictions](#understand-how-your-tensorflow-model-is-making-predictions)\n  - [20 Newsgroups data import script for Google Cloud AutoML Natural Language](#20-newsgroups-data-import-script-for-google-cloud-automl-natural-language)\n  - [How to use the Google Cloud Natural Language API](#how-to-use-the-google-cloud-natural-language-api)\n\n## Google Drive ZIP to GitHub Repository Exporter\n\nThis [notebook](zip-to-repo.ipynb) provides a streamlined workflow to take a ZIP file from your Google Drive and push its contents into a new or existing GitHub repository.\n\n## Identifying LLM \"Tells\": N-gram Analysis of Human vs. AI Text\n\nThis [notebook](detecting_ai_text_signatures.ipynb) aims to identify characteristic words and phrases (n-grams) that are statistically more likely to appear in text generated by a Large Language Model compared to human-written text. This process helps in understanding the stylistic differences between human and AI-generated content.\n\n## Querying a GitHub Codebase with Vertex AI RAG Engine\n\nThis [notebook](rag_codebase.ipynb) demonstrates how to use Vertex AI's\nRetrieval-Augmented Generation (RAG) capabilities to index the code files from a\npublic GitHub repository and then ask questions about that codebase using a generative model.\n\nIt uses [Vertex AI RAG Engine](https://cloud.google.com/vertex-ai/generative-ai/docs/rag-engine/rag-overview),\na component of the Vertex AI Platform.\n\n## Product Data Enrichment with Vertex AI\n\nThis [notebook](Product_Data_Enrichment_with_Vertex_AI.ipynb) demonstrates how\nto enrich your data using Generative AI with Vertex AI on Google Cloud.\n\nThe specific example is a retail use case for improving product description\nmetadata. Better product descriptions lead to more user engagement and higher\nconversion rates.\n\n## Causal Inference with Vertex AI AutoML Forecasting\n\nThis [notebook](causal_inference_with_vertex_ai_automl_forecasting.ipynb)\nintroduces the concept of causal inference. It shows how to estimate the effect\nof an intervention using the\n[tfcausalimpact](https://github.com/WillianFuks/tfcausalimpact) library and with\n[Vertex AI AutoML\nForecasting](https://cloud.google.com/vertex-ai/docs/training/automl-console#forecasting).\n\n## Medical Imaging notebooks using Vertex AI\n\nThe [pipeline notebook](medical_imaging_pipeline.ipynb) should be run first. It\nwill pre-process DICOM medical images in the dataset (which needs to be\ndownloaded prior to running). Then, it will create an AutoML model, and deploy\nit to an endpoint. It demonstrates how to build a pipeline using standard and\ncustom components.\n\nThe [custom training notebook](medical_imaging_custom_training.ipynb) can be run\nafterward. It shows how to train a TensorFlow model using the same managed\ndataset.\n\n## Understand how your TensorFlow model is making predictions\n\nThis [notebook](tensorflow-shap-college-debt.ipynb) demonstrates how to build a\nmodel using  [tf.keras](https://www.tensorflow.org/api_docs/python/tf/keras) and\nthen analyze its feature importances using the\n[SHAP](https://github.com/slundberg/shap) library.\n\nThe model predicts the expected debt-to-earnings ratio of a university's\ngraduates. It uses data from the US Department of Education's [College\nScorecard](https://collegescorecard.ed.gov/data/).\n\nMore details about the model can be found in the [blog\npost](https://medium.com/@kweinmeister/understand-how-your-tensorflow-model-is-making-predictions-d0b3c7e88500).\n\nYou can run the model [live in Colab with zero setup\nhere](https://colab.research.google.com/github/kweinmeister/notebooks/blob/master/tensorflow-shap-college-debt.ipynb).\n\nTo run it locally, make sure you have Jupyter installed (`pip install jupyter`).\n\nI've included the model code as a Jupyter notebook\n(`tensorflow-shap-college-debt.ipynb`). From the root directory run `jupyter\nnotebook` to start your notebook. Then navigate to `localhost:8888` and click on\n`tensorflow-shap-college-debt.ipynb`.\n\n## 20 Newsgroups data import script for Google Cloud AutoML Natural Language\n\nThis [notebook](20_newsgroups_automl.ipynb) downloads the [20 newsgroups\ndataset](https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html) using\nscikit-learn. This dataset contains about 18000 posts from 20 newsgroups, and is\nuseful for text classification. The script transforms the data into a pandas\ndataframe and finally into a CSV file readable by [Google Cloud AutoML Natural\nLanguage](https://cloud.google.com/natural-language/automl).\n\n## How to use the Google Cloud Natural Language API\n\nThis [notebook](google_cloud_natural_language_api.ipynb) demonstrates how to\nperform natural language tasks such as entity extraction, text classification,\nsentiment analysis, and syntax analysis using the [Google Cloud Natural Language\nAPI](https://cloud.google.com/natural-language/docs).\n\n\n[def]:\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkweinmeister%2Fnotebooks","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkweinmeister%2Fnotebooks","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkweinmeister%2Fnotebooks/lists"}