sample-notebooks
Sample notebooks that are published by IBM for IBM Data Science Experience.
File Explorer
- new archived nbs .zip
- sample
- Use python to recognize handwritten digits.zip
- A-c.zip
- D-F.zip
- H-L.zip
- M-R.zip
- S-T.zip
- Sample
- U01.zip
- U02.zip
- U03.zip
- V-Z.zip
- A Theoretical and Practical Review of Elasticity.html
- Access MySQL with Python.html
- Access MySQL with R.html
- Access PostgreSQL with Python.html
- Access PostgreSQL with R.html
- Action skill analysis for Watson Assistant.html
- Analyze energy consumption in buildings.html
- Analyze Watson Assistant Effectiveness.html
- Benders decomposition with decision optimization.html
- Building steel coils.html
- Calculating Thermodynamic Observables.html
- Car Complaint Analysis.html
- Complaint Classification.html
- Convert ONNX neural network from fixed axes to dynamic axes.html
- Data Security Broker with RAG.html
- Deploy_Pretrained_Sentiment_Model_Cloud.html
- Deploying a Decision Optimization Model with WML.html
- Determining Best Cut-Off.html
- Dialog Flow Analysis for Watson Assistant.html
- Dialog skill analysis for Watson Assistant.html
- Entity extraction on financial complaints.html
- Explore particulate matter data using IBM Cloud SQ.html
- Federated Learning FHE Demo.html
- Federated Learning Tensorflow 2 Demo Part 1 - for Admin.html
- Federated Learning Tensorflow 2 Demo Part 2 - for Party.html
- Federated Learning XGBoost Demo Part 1 - for Admin.html
- Federated Learning XGBoost Demo Part 2 - for Party.html
- Federated-Learning-Score-XGBoost-model-to-predict-income.html
- Financial complaint analysis.html
- Financial Portfolio Optimization.html
- Finding optimal locations of new stores using Deci.html
- Finding optimal locations of new stores using Decision Optimization.html
- German credit risk prediction with Scikit-learn for model monitoring.html
- House Building with worker skills.html
- How to make targeted offers to customers.html
- Incremental modeling with decision optimization.html
- Insights from New York car accident reports.html
- Key Point Summarization.html
- Machine Learning artifacts export and import.html
- Machine Learning artifacts management with REST API.html
- Machine Learning artifacts management.html
- Machine Learning for Equipment Maintenance - Pub.html
- Maximize company profits.html
- Measure Watson Assistant Performance.html
- Minimum Eigen Optimizer.html
- Model a Golomb ruler using DO.html
- Monitor credit risk model with Watson Openscale.html
- NotebooksCTA.jpeg
- Organize delivery with Decision Optimization.html
- Overlapping co-CLuster Recommendation algorithm _OCuLaR_.html
- Predictive Maintenance Optimization.html
- Promoting financial products to bank customers.html
- Quantum Application Classes.html
- Quantum Kernel Machine Learning.html
- Quantum Neural Networks.html
- Quantum PyTorch Connector.html
- RAG with SingleStore and watsonx.html
- Run Spark use cases for watsonx.data.html
- Sampling Panel Data for Machine Learning.html
- Sched Square.html
- Simple introduction to RAG with Discovery.html
- Simple introduction to retrieval augmented generation.html
- Small Steps to TensorFlow.html
- Space management.html
- Spatial Queries in PySpark.html
- Sudoku.html
- Test the model using the WML API Client.html
- The Nurse Assignment Problem.html
- The Pasta Production Problem.html
- The Unit Commitment Problem.html
- Times World University Ranking analysis.html
- Use AutoAI and timeseries data for PM2.html
- Use AutoAI predict credit risk (Batch).html
- Use AutoAI to predict credit risk.html
- Use custom software_spec to create statsmodels function describing data.html
- Use Decision Optimization to plan your diet.html
- Use decision optimization to schedule sports games.html
- Use decision trees and XGBoost to classify tumor data.html
- Use Lagrangian relaxation.html
- Use ONNX model converted from CatBoost.html
- Use ONNX model converted from LGBM.html
- Use ONNX model converted from PyTorch.html
- Use ONNX model converted from scikit-learn.html
- Use ONNX model converted from TensorFlow to recognize hand-written digits.html
- Use ONNX model converted from XGBoost.html
- Use PMML to predict Iris species.html
- Use scikit-learn and AI lifecycle capabilities to predict California house prices with ibm-watsonx-ai
- Use scikit-learn and custom library to predict temperature.html
- Use scikit-learn to recognize hand-written digits.html
- Use Spark for Python to load data and run SQL queries.html
- Use Spark for R to load data and run SQL queries.html
- Use Spark to predict business area for car rental company.html
- Use Spark to predict credit risk with WML.html
- Use Spark to predict customer churn (Batch).html
- Use Spark to predict product line with WML.html
- Use spatial indexing to query spatial data.html
- Use SPSS and batch deployment with DB2 to predict customer churn.html
- Use SPSS to predict customer churn.html
- Use statsmodels to forecast time series data.html
- Use the spatio-temporal library for location analytics.html
- Use Watsonx and Google to extract entities of climate fever.html
- Use watsonx and granite-20b-multilingual to support translation.html
- Use watsonx to analyze car rentals reviews.html
- Use Watsonx to generate advertising.html
- Use watsonx to manage Prompt Template assets and create deployment.html
- Use watsonx to tune Google 'flan-t5-xl' model with Consumer Financial Protection Bureau document.html
- Use watsonx to tune Meta llama-2-13b-chat model with CFPB document.html
- Use watsonx, and `mixtral_8x7b_instruct_v01_q` to generate code based on instruction.html
- Use watsonx, and `mixtral_8x7b_instruct_v01_q` to summarize legal Contracts documents.html
- Use watsonx, and codellama-34b-instruct-hf to generate code based on instruction.html
- Use watsonx, and Elasticsearch Python SDK to answer questions (RAG).html
- Use watsonx, and Google `flan-ul2` to analyze eXtensive Business Reporting Language (XBRL) tags of financial reports.html
- Use watsonx, and Google `flan-ul2` to extract the named entities of climate fever document.html
- Use watsonx, and Google `flan-ul2` to summarize Cybersecurity documents.html
- Use watsonx, and Meta `llama-2-70b-chat` to answer question about an article.html
- Use watsonx, and mixtral-8x7b-instruct-v01 to find sentiments of legal documents.html
- Use watsonx, Elasticsearch, and LangChain to answer questions (RAG).html
- Use XGBoost to classify tumors.html
- Use-watsonx-and-Meta-llama-2-70b-chat-to-answer-q.html
- Using Data Engine.html
- Using IBM Cloud SQL Query to analyze Log.html
- Using the Progress Listeners with CPLEX Optimizer.html
- Vector database agnostic integration with LangChain.html
- Visualize data with the matplotlib library.html
- jfk_weather_cleaned.csv
- notebook:Part_1_-_Data_Cleaning_WiVsaamRN.ipynb
- notebook:Part_2_-_Data_Analysis_wKXWkDSTR.ipynb
- readme
- asset_terms.json
- column_info.json
- connection.json
- data_asset.json
- data_profile.json
- do_decision_asset.json
- do_scenario_asset.json
- do_solve_asset.json
- folder_asset.json
- notebook.json
- omrs_entity.json
- omrs_relationship.json
- omrs_relationship_message.json
- policy_transform.json
- readme
- Part 1 - Data Cleaning.html
- project.json
- project:readme.json
- readme
- A Theoretical and Practical Review of Elasticity.ipynb
- Access MySQL with Python.ipynb
- Access MySQL with R.ipynb
- Access PostgreSQL with Python.ipynb
- Access PostgreSQL with R.ipynb
- Action skill analysis for Watson Assistant.ipynb
- Analyze energy consumption in buildings.ipynb
- Analyze Watson Assistant Effectiveness.ipynb
- Benders decomposition with Decision Optimization.ipynb
- Building steel coils.ipynb
- Calculating Thermodynamic Observables.ipynb
- Car Complaint Analysis.ipynb
- Complaint Classification.ipynb
- Convert ONNX neural network from fixed axes to dynamic axes.ipynb
- Data Security Broker with RAG.ipynb
- Deploy_Pretrained_Sentiment_Model_Cloud.ipynb
- Deploying a Decision Optimization Model with WML.ipynb
- Determining Best Cut-Off.ipynb
- Dialog Flow Analysis for Watson Assistant.ipynb
- Dialog skill analysis for Watson Assistant.ipynb
- Dummy.md
- Entity extraction on financial complaints.ipynb
- Explore particulate matter data using IBM Cloud SQL query.ipynb
- Federated Learning FHE Demo.ipynb
- Federated Learning Tensorflow 2 Demo Part 1 - for Admin.ipynb
- Federated Learning Tensorflow 2 Demo Part 2 - for Party.ipynb
- Federated Learning XGBoost Demo Part 1 - for Admin.ipynb
- Federated Learning XGBoost Demo Part 2 - for Party.ipynb
- Financial complaint analysis.ipynb
- Financial Portfolio Optimization.ipynb
- Finding optimal locations of new stores using DO.ipynb
- German credit risk prediction with Scikit-learn for model monitoring.ipynb
- House Building with worker skills.ipynb
- How to make targeted offers to customers.ipynb
- Incremental modeling with Decision Optimization.ipynb
- Insights from New York car accident reports.ipynb
- Introduction-to-retrieval-augmented-generation.ipynb
- Key Point Summarization.ipynb
- Machine Learning artifacts export and import.ipynb
- Machine Learning artifacts management with REST API.ipynb
- Machine Learning artifacts management.ipynb
- Machine Learning for Equipment Maintenance - Pub.ipynb
- Maximizing the profit of an oil company.ipynb
- Measure Watson Assistant Performance.ipynb
- Minimum Eigen Optimizer.ipynb
- Model a Golomb ruler using DO.ipynb
- Monitor credit risk model with Watson Openscale.ipynb
- Organize delivery with Decision Optimization.ipynb
- Overlapping co-CLuster Recommendation algorithm _OCuLaR_.ipynb
- Predictive Maintenance Optimization.ipynb
- Promoting financial products to bank customers.ipynb
- Quantum Application Classes.ipynb
- Quantum Kernel Machine Learning.ipynb
- Quantum Neural Networks.ipynb
- Quantum PyTorch Connector.ipynb
- RAG with SingleStore and watsonx.ipynb
- Run Spark use cases for watsonx.data.ipynb
- Sampling methods for panel data.ipynb
- Sched Square.ipynb
- Simple introduction to RAG with Discovery.ipynb
- Simple-Introduction-to-retrieval-augmented-generation.ipynb
- Small Steps to TensorFlow.ipynb
- Space management.ipynb
- Spatial Queries in PySpark.ipynb
- Sudoku.ipynb
- Test a model using the WML API client.ipynb
- The Nurse Assignment Problem.ipynb
- The Pasta Production Problem.ipynb
- The Unit Commitment Problem.ipynb
- Times World University Ranking analysis.ipynb
- tree-view.atloc
- Use AutoAI and timeseries data for PM2.5.ipynb
- Use AutoAI to predict credit risk.ipynb
- Use custom software_spec to create statsmodels function describing data.ipynb
- Use Decision Optimization to plan your diet.ipynb
- Use Decision Optimization to schedule sports games.ipynb
- Use decision trees and XGBoost to classify tumors.ipynb
- Use Lagrangian relaxation.ipynb
- Use ONNX model converted from CatBoost.ipynb
- Use ONNX model converted from LGBM.ipynb
- Use ONNX model converted from PyTorch.ipynb
- Use ONNX model converted from scikit-learn.ipynb
- Use ONNX model converted from TensorFlow to recognize hand-written digits.ipynb
- Use ONNX model converted from XGBoost.ipynb
- Use PMML to predict Iris species.ipynb
- Use scikit-learn and AI lifecycle to predict Boston house prices.ipynb
- Use scikit-learn and custom library to predict temperature.ipynb
- Use scikit-learn to recognize hand-written digits.ipynb
- Use Spark for Python to load data and run SQL queries.ipynb
- Use Spark for R to load data and run SQL queries.ipynb
- Use Spark to predict business area for car rental company.ipynb
- Use Spark to predict credit risk with WML.ipynb
- Use Spark to predict customer churn.ipynb
- Use Spark to predict product line with WML.ipynb
- Use spatial indexing to query spatial data.ipynb
- Use SPSS and batch deployment with DB2 to predict customer churn.ipynb
- Use SPSS to predict customer churn.ipynb
- Use the spatio-temporal library for location analytics.ipynb
- Use watsonx and codellama-34b-instruct-hf to generate code based on instruction.ipynb
- Use watsonx and Elasticsearch Python SDK to answer questions (RAG).ipynb
- Use watsonx and Google flan-t5-xxl to generate advertising.ipynb
- Use watsonx and Google flan-ul2 to analyze XBRL tags of financial reports.ipynb
- Use Watsonx and Google flan-ul2 to extract entities of climate fever document.ipynb
- Use watsonx and Google flan-ul2 to summarize Cybersecurity documents.ipynb
- Use watsonx and granite-20b-multilingual to support translation.ipynb
- Use watsonx and IBM granite-13b-instruct-v2 to analyze car rental customer satisfaction from text.ipynb
- Use watsonx and Meta llama-2-70b-chat to answer question about an article.ipynb
- Use watsonx and mixtral-8x7b-instruct-v01 to analyze sentiments of legal documents.ipynb
- Use watsonx and mixtral_8x7b_instruct_v01_q to generate code based on instruction.ipynb
- Use watsonx and mixtral_8x7b_instruct_v01_q to summarize legal Contracts documents.ipynb
- Use watsonx to manage Prompt Template assets and create deployment.ipynb
- Use watsonx to tune Google flan-t5-xl model with CFPB document.ipynb
- Use watsonx to tune Meta llama-2-13b-chat model with CFPB document.ipynb
- Use watsonx, Elasticsearch, and LangChain to answer questions (RAG).ipynb
- Use XGBoost to classify tumors.ipynb
- Using Data Engine.ipynb
- Using IBM Cloud SQL Query to analyze Log.ipynb
- Using the Progress Listeners with CPLEX Optimizer.ipynb
- Vector database agnostic integration with LangChain.ipynb
- Visualize data with the matplotlib library.ipynb
- Comments-Organizer-Project.zip
- COVID-19-Tracking-with-IBM-DataStage.zip
- Create-Glossary-from-Files-with-AI-powered-tool.zip
- Create.RAG.vector.stores.for.watsonx.Code.Assistant.zip
- DAX-Weather-Project.zip
- Distribution-Marketing-Project.zip
- Effective-Farming-Project.zip
- Federated-Learning-Tutorial-Project.zip
- Federated-Learning-XGBoost-Demo.zip
- Financial_Markets_Customer_Attrition_Prediction_Industry_Accelerator.zip
- Financial_Markets_Customer_Life_Event_Prediction.1.zip
- Financial_Markets_Customer_Offer_Affinity_Industry_Accelerator.zip
- Financial_Markets_Customer_Segmentation_Industry_Accelerator.zip
- Getting started with watsonx governance.zip
- Groningen-Meaning-Bank-Project.zip
- Historical_Product_Demand_Modeler_Training.csv
- Hospital-Readmission-Prediction.zip
- IBM-Debater-Sentiment-Composition-Lexicons.zip
- IBM-Debater-Thematic-Clustering-of-Sentences.zip
- Insurance-Loss-Estimation-Using-Remote-Sensing-Data-Industry-Accelerator.zip
- Insurance-Pricing-Optimization-Project.zip
- Inventory-Management.zip
- Mask-Inventory-Management.zip
- Network-Design.zip
- Oil-Reservoir-Simulation-Project.zip
- Predict-customer-interest-to-optimize-a-campaign-with-ML-+-DO.zip
- Predicting Healthcare Costs from Claims Data.zip
- QnA-with-RAG-Accelerator-v2.0-wxaas-GC3.zip
- readme.md
- Sales-and-Operations-Planning.zip
- Sales-Prediction-using-Weather-Company-Data.zip
- Shelf-Space-Optimization.zip
- TensorFlow-Speech-Commands-Project.zip
- Text-Analysis-with-Watson-Natural-Language-Processing---Runtime-24.1.zip
- Train-AutoAI-and-reference-model.zip
- Utilities-customer-attrition-prediction-sample-project.zip
- Utilities_Customer_Micro_Segmentation_Industry_Accelerator.zip
- Utilities_Demand_Response_Program_Propensity_Industry_Accelerator.zip
- Utilities_Payment_Risk_Prediction_Industry_Accelerator.zip
- Data Skipping Sample for Python.ipynb
- Data Skipping Sample for Scala.ipynb
- Deploy a Python Script that uses COS data.ipynb
- Deploy a Shiny App using the WML Python.ipynb
- Deploy_a_custom_scikit-learn_estimator_to_WML.ipynb
- Deployment scaling with WML.ipynb
- Export and import a deployment space.ipynb
- Readme.md
- Save__compress__and_deploy_a_Keras_model.ipynb
- Train model on cloud and deploy on local cluster.ipynb
- Use SPSS to predict customer churn Batch.ipynb
- Use TensorFlow to predict hand-written digits.ipynb
- Use_Core_ML_model_to_predict_Boston_house_prices.ipynb
- Use_PMML_to_predict_species_of_irises.ipynb
- Use_Python_function_feature_to_scrape_a_webpage.ipynb
- Use_scikit-learn_to_predict_the_car_price_(Script).ipynb
- Use_scikit-learn_to_predict_the_price_of_a_car.ipynb
- Use_SPSS_to_predict_customer_churn.ipynb
- Use_XGBoost_to_classify_tumors_(Batch).ipynb
- Using custom components with tf.ipynb
- bank-full.csv
- bank-payload.csv
- GoSales.csv
- Readme.md
- readme.md
- Federated-Learning-Demo-for-CP4D.zip
- Readme.md
- Readme.md
- Federated Learning TF Demo Part 1.html
- Federated Learning TF Demo Part 2.html
- Federated_Learning_XGBoost_Demo_Part_1.html
- Federated_Learning_XGBoost_Demo_Part_2.html
- Readme.md
- Federated Learning TF Demo Part 1.ipynb
- Federated Learning TF Demo Part 2.ipynb
- Federated_Learning_XGBoost_Demo_Part_1.ipynb
- Federated_Learning_XGBoost_Demo_Part_2.ipynb
- Working with ibm-watson-studio-lib in CPD.ipynb
- Readme.md
- Deploying.a.Decision.Optimization.Model.with.Watson.ipynb
- Federated Learning TF Demo Part 1.ipynb
- Federated Learning TF Demo Part 2.ipynb
- Federated_Learning_XGBoost_Demo_Part_1.ipynb
- Federated_Learning_XGBoost_Demo_Part_2.ipynb
- Save__compress__and_deploy_a_Keras_model_aEmdZbHco.ipynb
- Train model on cloud and deploy on local cluster.ipynb
- Use_PMML_to_predict_species_of_irises_xI-bH_Ms8.ipynb
- Use_Python_function_feature_to_scrape_a__ndTSmTZh9.ipynb
- Use_scikit-learn_to_predict_car_price_Ba__iWpJLr4U.ipynb
- Use_scikit-learn_to_predict_handwritten__J31Au0jvj.ipynb
- Use_scikit-learn_to_predict_the_price_of_S0wRYtxMd.ipynb
- Use_XGBoost_to_classify_tumors_udXnYEL7y.ipynb
- Watson OpenScale and ML Engine.ipynb
- readme.md
- Car_Complaint_Analysis.ipynb
- Complaint_Classification.ipynb
- Deploy_Pretrained_Sentiment_Model.ipynb
- Entity_extraction_on_financial_complaints.ipynb
- Financial_complaint_analysis.ipynb
- Car_Complaint_Analysis.ipynb
- Complaint_Classification.ipynb
- Deploy_Pretrained_Sentiment_Model.ipynb
- Entity_extraction_on_financial_complaints.ipynb
- Financial_complaint_analysis.ipynb
- Federated Learning TF Demo Part 1.ipynb
- Federated Learning TF Demo Part 2.ipynb
- Federated_Learning_XGBoost_Demo_Part_1.ipynb
- Federated_Learning_XGBoost_Demo_Part_2.ipynb
- NLP-Example-Project-RT22-1.zip
- NLP-Example-Project-RT22-2.zip
- readme.md
- FeatureGroup-Project.zip
- NLP-Example-Project-RT23-1.zip
- readme.md
- Car_Complaint_Analysis.ipynb
- Complaint_Classification.ipynb
- Deploy_Pretrained_Sentiment_Model.ipynb
- Entity_extraction_on_financial_complaints.ipynb
- Federated Learning FHE.ipynb
- Federated_Learning_TF_Demo_Part_1.ipynb
- Federated_Learning_TF_Demo_Part_2.ipynb
- Federated_Learning_XGBoost_Demo_Part_1.ipynb
- Federated_Learning_XGBoost_Demo_Part_2.ipynb
- Financial_complaint_analysis.ipynb
- readme.md
- FeatureGroup-Project.zip
- NLP-Example-Project-RT23-1.zip
- readme.md
- Introduction-to-retrieval-augmented-generation.ipynb
- Working with ibm-watson-studio-lib in CPD.ipynb
- NLP-Example-Project-RT-24.1.zip
- Car_Complaint_Analysis.ipynb
- Complaint_Classification.ipynb
- Deploy_Pretrained_Sentiment_Model.ipynb
- Entity_extraction_on_financial_complaints.ipynb
- Federated Learning FHE.ipynb
- Federated_Learning_TF_Demo_Part_1.ipynb
- Federated_Learning_TF_Demo_Part_2.ipynb
- Federated_Learning_XGBoost_Demo_Part_1.ipynb
- Federated_Learning_XGBoost_Demo_Part_2.ipynb
- Financial_complaint_analysis.ipynb
- Hotel_Review_Analysis.ipynb
- Introduction-to-retrieval-augmented-generation.ipynb
- Populate vector store.ipynb
- README.md
- Spatial Queries in PySpark.ipynb
- Use spatial indexing to query spatial data.ipynb
- Use the spatio-temporal library for location analytics.ipynb
- Managing Users in WML Server Documentation.ipynb
- README.md
- Use Core ML model to predict Boston house prices.ipynb
- Use PMML to predict species of irises.ipynb
- Use Python function feature to scrape a webpage.ipynb
- Use scikit-learn to predict hand-written digits.ipynb
- Use scikit-learn to predict the price of a car - Batch deployment.ipynb
- Use scikit-learn to predict the price of a car.ipynb
- Use SPSS to predict customer churn.ipynb
- Use XGBoost to classify tumors-2.ipynb
- Backlog.csv
- dataset_austin.csv
- Historical_Product_Demand_Modeler_Forecast.csv
- Historical_Product_Demand_per_Product.csv
- Locations.csv
- Monthly_Demand_Inventory.csv
- Next_Three_Month_Forecasts.csv
- Product_Category_Legend.csv
- Production_and_Location.csv
- Readme.md
- SupplierCapacity.csv
- Supply_Chain_Accelerator_Application_24th_May.zip
- SupplyChain-glossary-categories.csv
- Transposed_Forecast_Table.csv
- adult_sklearn_data_handler.py
- airline-data.csv
- ANDonBlochSphere_I.png
- ANDonBlochSphere_II.png
- ANDonBlochSphere_III.png
- ANDonBlochSphereI.png
- ANDonBlochSphereII.png
- ANDonBlochSphereIII.png
- app.R.zip
- CARS4U-AI-function-example.js
- CARS4U-AI-models-example.js
- CARS4U-altered-training-data.csv
- CARS4U-sample-payload-logging.csv
- copycredentials.png
- copyservicecredentials.png
- Customer-Care-Sample-Skill-action.json
- Customer-Care-Sample-Skill-dialog_test.tsv
- Customer-Care-Sample-Skill-dialog_train.json
- cycle_gan_pytorch.zip
- dialog.png
- fhe_1.jpg
- fhe_2.jpg
- fhe_3.jpg
- finalflow1.png
- gan_fashion_mnist.zip
- gmb_subset_full.txt
- HealthcareDemoOutput.png
- HW_ApplicationDashboard.png
- HW_Console_Log.png
- ibm_watson_machine_learning-1.0.316-py3-none-any.whl
- install_fl_rt22.2_macos.sh
- install_fl_rt23.1_macos.sh
- manage.png
- Measure Watson Assistant Performance.ipynb
- messagehubflow.gif
- mnist_keras_data_handler.py
- mtcars.csv
- pt_mnist_init_model.zip
- runningflow2.png
- seed data examples.zip
- ShinyWebApp.png
- sklearn_arima-0.1.zip
- SubmitJob_ApplicationDashboard.png
- tensorflow_input.zip
- test_cos_script.py
- test_set.csv
- tf_mnist_init_model.zip
- tf_mnist_model.zip
- topic.png
- transform_pred_script.py
- UnitedStatesDemographicMeasuresEducation2015_ColumnDetails.csv
- UnitedStatesDemographicMeasuresHealthInsurance2015_ColumnDetails.csv
- UnitedStatesDemographicMeasuresHousing2015_ColumnDetails.csv
- UnitedStatesDemographicMeasuresIncome2015_ColumnDetails.csv
- UnitedStatesDemographicMeasuresOccupation2015_ColumnDetails.csv
- UnitedStatesDemographicMeasuresPoverty2015_ColumnDetails.csv
- wca_rag_lib-0.0.0.tar.gz
- Access+dashDB+and+DB2+with+Scala.html
- AnalyzeEnergyConsumptionInBuildings.html
- Learn basics about notebooks and Apache Spark.html
- Welcome+to+PixieDust.html
- README.md
- Save__compress__and_deploy_a_Keras_model.ipynb
- Use_PMML_to_predict_species_of_irises.ipynb
- Use_Python_function_feature_to_scrape_a_webpage.ipynb
- Use_scikit-learn_to_predict_hand-written_digits.ipynb
- Use_scikit-learn_to_predict_the_price_of_a car.ipynb
- Use_XGBoost_to_classify_tumors.ipynb
- Use_XGBoost_to_classify_tumors_(Batch).ipynb
- Save, compress, and deploy a Keras model.html
- test
- Use PMML to predict Iris species.html
- Use Python function to scrape a webpage.html
- Use Scikit-learn to predict car price (Batch deploy).html
- Use Scikit-learn to predict car price.html
- Predict_handwritten_digits_ycjCHRVq6.ipynb
- Save__compress__and_deploy_a_Keras_model_coqFqWuTy.ipynb
- Use_PMML_to_predict_iris_species_(Batch)_BXELwOcNH.ipynb
- Use_Python_function_to_scrape_a_webpage_SnGOBzj1m.ipynb
- Use_scikit-learn_to_predict_car_price_(B_bnHwl6yL-.ipynb
- Use_scikit-learn_to_predict_car_price_2I6wlE_SH.ipynb
- Use_SPSS_to_predict_customer_churn_XIRr6mDoO.ipynb
- Use_XGBoost_to_classify_tumors_(Batch)_BrXWuAVtQ.ipynb
- readme.md
- readme.md
- readme.md
- Bike_Sharing_Finetuning_with_Exogenous.html
- Bike_Sharing_Finetuning_with_Exogenous.ipynb
- Efficient Data Labeling in Label Sleuth for Binary Classification in watsonx.html
- Efficient Data Labeling in Label Sleuth for Binary Classification in watsonx.ipynb
- Embedding model agnostic integration with LangChain.html
- Embedding model agnostic integration with LangChain.ipynb
- GenSL_SQLGen_Gosales_watsonx_ai.html
- GenSL_SQLGen_Gosales_watsonx_ai.ipynb
- Grouping Search in Milvus watsonx.data.ipynb
- Grouping Search in Milvus watsonx.html
- IBM Watsonx Governance Evaluation Studio Getting Started.html
- IBM Watsonx Governance Evaluation Studio Getting Started.ipynb
- IBM Watsonx Governance Governed Agentic Catalog.html
- IBM Watsonx Governance Governed Agentic Catalog.ipynb
- Prompt tuning for binary classification with watsonx.html
- Prompt tuning for binary classification with watsonx.ipynb
- Prompt tuning for multi-class classification with watsonx.html
- Prompt tuning for multi-class classification with watsonx.ipynb
- Prompt tuning for multi-label classification with watsonx.html
- Prompt tuning for multi-label classification with watsonx.ipynb
- Use AutoAI RAG with custom foundation model.html
- Use AutoAI RAG with custom foundation model.ipynb
- Use AutoAI RAG with watsonx Text Extraction service.html
- Use AutoAI RAG with watsonx Text Extraction service.ipynb
- Use watsonx and Elasticsearch Python SDK to answer questions (RAG).html
- Use watsonx and Elasticsearch Python SDK to answer questions (RAG).ipynb
- Use watsonx and granite-8b-code-instruct to generate code based on instruction.html
- Use watsonx and granite-8b-code-instruct to generate code based on instruction.ipynb
- Use watsonx and LangChain to make a series of calls to a language model.html
- Use watsonx and LangChain to make a series of calls to a language model.ipynb
- Use watsonx and Meta llama-3-3-70b-instruct to answer question about an article.html
- Use watsonx and Meta llama-3-3-70b-instruct to answer question about an article.ipynb
- Use watsonx and Meta llama-3-70b-instruct to answer question about an article.html
- Use watsonx and mistral-small-3-1-24b-instruct-2503 to find sentiments of legal documents.html
- Use watsonx and mistral-small-3-1-24b-instruct-2503 to find sentiments of legal documents.ipynb
- Use watsonx to manage Prompt Template assets and create deployment.html
- Use watsonx to manage Prompt Template assets and create deployment.ipynb
- Use watsonx to tune IBM granite-13b-instruct-v2 model with Car Rental Company customer satisfaction document.html
- Use watsonx to tune IBM granite-13b-instruct-v2 model with Car Rental Company customer satisfaction document.ipynb
- Use watsonx, and LangChain to make a series of calls to a language model.html
- Use watsonx, and Meta llama-3-70b-instruct to answer question about an article.ipynb
- Use watsonx, Chroma, and LangChain to answer questions (RAG).html
- Use watsonx, Chroma, and LangChain to answer questions (RAG).ipynb
- Use watsonx, Elasticsearch, and LangChain to answer questions (RAG).html
- Use watsonx, Elasticsearch, and LangChain to answer questions (RAG).ipynb
- readme.md
- Use Core ML model to predict Boston house prices.ipynb
- Use PMML to predict species of irises.ipynb
- Use Python function feature to scrape a webpage.ipynb
- Use scikit-learn to predict hand-written digits.ipynb
- Use scikit-learn to predict the price of a car - Batch deployment.ipynb
- Use scikit-learn to predict the price of a car.ipynb
- Use SPSS to predict customer churn.ipynb
- Use XGBoost to classify tumors-2.ipynb
- readme.md
- README.md
# Use via CDN
jsDelivrjsDelivr serves any public GitHub repository as a CDN with zero setup. Pick a version and a file to get a ready-to-paste link and snippet.
Link
Example
// repository documentation
Was this content helpful?
(0 ratings)