{"id":14958736,"url":"https://github.com/laminetourelab/tutorial","last_synced_at":"2025-10-24T16:30:33.125Z","repository":{"id":64833702,"uuid":"578536054","full_name":"LamineTourelab/Tutorial","owner":"LamineTourelab","description":"Tutorials on machine learning, artificial intelligence in general and in biomedical research. 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I tried to do all the code in colab to make it easy for reproducibility. Some code are not mine!.\n\n# `Links`\n\n- Documentation: https://laminetourelab.github.io\n- Contributing: https://github.com/LamineTourelab/Tutorial/graphs/contributors\n\nThe Mkdocs was used for the creation of the website.\n\n# `Publish your Markdown docs on GitHub Pages`\n\n\nFirst install mkdocs tools with pip:\n```\nconda create -n mkdoc python=3.11\nconda activate mkdoc\npip install -U mkdocs\npip install mkdocs-jupyter\npip install python-markdown-math\npip install markdown-callouts\npip install mdx-gh-links\npip install mkdocs-click\n```\nTo create your docs source project, you will need the repository source cloned:\n\n```mkdocs new GITHUB_repo```\nNow if you go at this repo you will fine these files:\n\n```\n./\n-- docs/\n-- mkdocs.yml\n```\nRun ```mkdocs serve``` to test your browser.\n\nThe deployment can be done inside your github.io repository as follow:\n\n```mkdocs gh-deploy --config-file ../GITHUB_repo/mkdocs.yml --remote-branch main```\n\nFor more details visit the [mkdocs.org](https://www.mkdocs.org/) website.\n\n# `User Guide Index`\n\n## `Bioinformatics`\n\n- [RNA Velocity analysis scVelo](https://github.com/LamineTourelab/Tutorial/blob/main/Bioinformatics/SingleCellData_Tutorial/RNA_Velocity_analysis_scVelo.ipynb)\n- [SingleCell Data Preprocessing with scanpy](https://github.com/LamineTourelab/Tutorial/blob/main/Bioinformatics/SingleCellData_Tutorial/SingleCellData_Preprocessing_with_scanpy.ipynb)\n- [Trajectory inference With CellRank](https://github.com/LamineTourelab/Tutorial/blob/main/Bioinformatics/SingleCellData_Tutorial/Trajectory_inference_With_CellRank.ipynb)\n\n## ` Medical Image Analysis`\n- [Chest X Ray Images classification with DenseNet121 and explain with GradCam on tensorflow 2](https://github.com/LamineTourelab/Tutorial/blob/main/Images/Xray_classification_with_densenet121_and_gradcam.ipynb)\n  \n## `Machine learning`\n- [Introduction to Autoencoder](https://github.com/LamineTourelab/Tutorial/blob/main/machine%20learning/Autoencoder.ipynb)\n- [Decision Trees using iris dataset](https://github.com/LamineTourelab/Tutorial/blob/main/machine%20learning/DecisionTrees_using_iris_dataset.ipynb)\n- [Transfer learning using VGG16](https://github.com/LamineTourelab/Tutorial/blob/main/machine%20learning/Transfer_learning_using_VGG16.ipynb)\n- [Introduction to tensorflow API tutorial](https://github.com/LamineTourelab/Tutorial/blob/main/machine%20learning/tensorflow_API_tutorial.ipynb)\n- [Introduction to tensor basics operation](https://github.com/LamineTourelab/Tutorial/blob/main/machine%20learning/tensor_basics_operation.ipynb)\n\n## `Explainable AI`\n\n- [Explainable xgboot model using shap and lime](https://github.com/LamineTourelab/Tutorial/blob/main/Explainable%20AI/explainability_shap%26lime.ipynb)\n- [Explain tensorflow model using Shap and Model Weights ](https://github.com/LamineTourelab/Tutorial/blob/main/Explainable%20AI/Explainable_tensorflow_model_Shap.ipynb)\n- [Explainable tensorflow multiclass model using Shap and model weights](https://github.com/LamineTourelab/Tutorial/blob/main/Explainable%20AI/Explainable_tensorflow_multiclass_model_using_Shap_and_model_weights.ipynb)\n- [Chest X Ray Images classification with DenseNet121 and explain with GradCam on tensorflow 2](https://github.com/LamineTourelab/Tutorial/blob/main/Explainable%20AI/Xray_classification_with_densenet121_and_gradcam.ipynb)\n\n  ### Note \nThere is an ongoing project in explainable AI applied to healthcare in collaboration with `Salvatore RAIELI` (his [github](https://github.com/SalvatoreRa)). The source code can be found at [https://github.com/SalvatoreRa/explanaibleAI](https://github.com/SalvatoreRa/explanaibleAI). Don't hesitate to reach out for collaboration or any question.\n\n## `Data visualization`\n- [Exploratory data analysis using matplotlib plotly express and Dash](https://github.com/LamineTourelab/Tutorial/blob/main/Data%20Visualization/EDA_matplotlib_Dashboard_Dataviz.ipynb)\n- [Shiny R App for genomics data](https://github.com/LamineTourelab/Tutorial/blob/main/Data%20Visualization/Shiny%20App%20in%20genomics/app.R)\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flaminetourelab%2Ftutorial","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flaminetourelab%2Ftutorial","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flaminetourelab%2Ftutorial/lists"}