[Tutorialsplanet.NET] Udemy - Hyperparameter Optimization for Machine Learning

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[Tutorialsplanet.NET] Udemy - Hyperparameter Optimization for Machine Learning

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收录时间：2024-02-04
最近下载：2024-11-19

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文件列表

  • 6. Bayesian Optimization/6. Sequential Model-Based Optimization.mp4 119.6 MB
  • 8. Scikit-Optimize/13. Optimizing Hyperparameters of a CNN.mp4 116.8 MB
  • 6. Bayesian Optimization/16. Scikit-Optimize - Neuronal Networks.mp4 116.8 MB
  • 7. Other SMBO Algorithms/2. SMAC Demo.mp4 104.4 MB
  • 6. Bayesian Optimization/12. Scikit-Optimize - 1-Dimension.mp4 101.9 MB
  • 9. Hyperopt/3. Search space configuration and distributions.mp4 98.6 MB
  • 10. Optuna/6. Optimizing hyperparameters of a CNN.mp4 89.4 MB
  • 6. Bayesian Optimization/8. Multivariate Gaussian Distribution.mp4 88.0 MB
  • 6. Bayesian Optimization/11. Acquisition Functions.mp4 86.3 MB
  • 5. Basic Search Algorithms/9. Random Search with Hyperopt.mp4 85.1 MB
  • 4. Cross-Validation/2. Cross-Validation schemes.mp4 83.7 MB
  • 6. Bayesian Optimization/9. Gaussian Process.mp4 79.9 MB
  • 10. Optuna/8. Evaluating the search with Optuna's built in functions.mp4 74.5 MB
  • 6. Bayesian Optimization/5. Bayes Rule.mp4 71.1 MB
  • 9. Hyperopt/7. Optimizing multiple ML models simultaneously.mp4 69.8 MB
  • 4. Cross-Validation/3. Estimating the model generalization error with CV - Demo.mp4 69.0 MB
  • 9. Hyperopt/5. Search algorithms.mp4 68.0 MB
  • 3. Performance metrics/5. Creating your own metrics.mp4 67.6 MB
  • 10. Optuna/4. Search algorithms.mp4 66.6 MB
  • 2. Hyperparameter Tuning - Overview/1. Parameters and Hyperparameters.mp4 65.3 MB
  • 1. Introduction/1. Introduction.mp4 64.7 MB
  • 5. Basic Search Algorithms/4. Grid Search - Demo.mp4 62.3 MB
  • 10. Optuna/5. Optimizing multiple ML models with simultaneously.mp4 61.1 MB
  • 4. Cross-Validation/1. Cross-Validation.mp4 60.5 MB
  • 4. Cross-Validation/4. Cross-Validation for Hyperparameter Tuning - Demo.mp4 59.6 MB
  • 9. Hyperopt/6. Evaluating the search.mp4 58.9 MB
  • 4. Cross-Validation/8. Nested Cross-Validation - Demo.mp4 58.0 MB
  • 2. Hyperparameter Tuning - Overview/2. Hyperparameter Optimization.mp4 53.3 MB
  • 7. Other SMBO Algorithms/7. TPE with Hyperopt.mp4 52.4 MB
  • 4. Cross-Validation/7. Nested Cross-Validation.mp4 52.3 MB
  • 5. Basic Search Algorithms/8. Random Search with Scikit-Optimize.mp4 50.7 MB
  • 6. Bayesian Optimization/4. Joint and Conditional Probabilities.mp4 48.4 MB
  • 3. Performance metrics/4. Scikit-learn metrics.mp4 48.1 MB
  • 5. Basic Search Algorithms/7. Random Search with Scikit-learn.mp4 46.3 MB
  • 6. Bayesian Optimization/3. Bayesian Inference - Introduction.mp4 45.5 MB
  • 4. Cross-Validation/6. Group Cross-Validation - Demo.mp4 45.4 MB
  • 5. Basic Search Algorithms/2. Manual Search.mp4 45.2 MB
  • 3. Performance metrics/2. Classification Metrics (Optional).mp4 45.0 MB
  • 7. Other SMBO Algorithms/4. TPE Procedure.mp4 44.4 MB
  • 10. Optuna/2. Optuna main functions.mp4 43.6 MB
  • 5. Basic Search Algorithms/6. Random Search.mp4 43.0 MB
  • 4. Cross-Validation/5. Special Cross-Validation schemes.mp4 42.9 MB
  • 8. Scikit-Optimize/6. Random search.mp4 40.0 MB
  • 10. Optuna/7. Optimizing a CNN - extended.mp4 39.0 MB
  • 8. Scikit-Optimize/14. Analyzing the CNN search.mp4 38.9 MB
  • 6. Bayesian Optimization/17. Scikit-Optimize - CNN - Search Analysis.mp4 38.9 MB
  • 9. Hyperopt/1. Hyperopt.mp4 38.1 MB
  • 6. Bayesian Optimization/13. Scikit-Optimize - Manual Search.mp4 37.7 MB
  • 8. Scikit-Optimize/7. Bayesian search with Gaussian processes.mp4 36.9 MB
  • 1. Introduction/2. Course curriculum.mp4 36.6 MB
  • 6. Bayesian Optimization/7. Gaussian Distribution.mp4 36.3 MB
  • 7. Other SMBO Algorithms/1. SMAC.mp4 34.2 MB
  • 6. Bayesian Optimization/14. Scikit-Optimize - Automatic Search.mp4 32.4 MB
  • 8. Scikit-Optimize/11. Bayesian search with Scikit-learn wrapper.mp4 32.4 MB
  • 6. Bayesian Optimization/1. Sequential Search.mp4 32.1 MB
  • 6. Bayesian Optimization/10. Kernels.mp4 31.8 MB
  • 9. Hyperopt/4. Sampling from nested spaces.mp4 30.1 MB
  • 8. Scikit-Optimize/10. Parallelizing a Bayesian search.mp4 27.4 MB
  • 7. Other SMBO Algorithms/6. TPE - why tree-structured.mp4 27.1 MB
  • 5. Basic Search Algorithms/1. Basic Search Algorithms - Introduction.mp4 26.7 MB
  • 8. Scikit-Optimize/12. Changing the kernel of a Gaussian Process.mp4 26.3 MB
  • 6. Bayesian Optimization/15. Scikit-Optimize - Alternative Kernel.mp4 26.2 MB
  • 8. Scikit-Optimize/1. Scikit-Optimize.mp4 26.0 MB
  • 8. Scikit-Optimize/3. Hyperparameter Distributions.mp4 25.3 MB
  • 7. Other SMBO Algorithms/5. TPE hyperparameters.mp4 24.4 MB
  • 8. Scikit-Optimize/9. Bayesian search with GBMs.mp4 24.1 MB
  • 8. Scikit-Optimize/8. Bayesian search with Random Forests.mp4 24.1 MB
  • 6. Bayesian Optimization/2. Bayesian Optimization.mp4 23.5 MB
  • 10. Optuna/1. Optuna.mp4 22.3 MB
  • 7. Other SMBO Algorithms/3. Tree-structured Parzen Estimators - TPE.mp4 20.2 MB
  • 5. Basic Search Algorithms/5. Grid Search with different hyperparameter spaces.mp4 19.3 MB
  • 3. Performance metrics/6. Using Scikit-learn metrics.mp4 18.7 MB
  • 8. Scikit-Optimize/4. Defining the hyperparameter space.mp4 18.0 MB
  • 3. Performance metrics/3. Regression Metrics (Optional).mp4 17.4 MB
  • 5. Basic Search Algorithms/3. Grid Search.mp4 17.1 MB
  • 1. Introduction/3. Course aim and knowledge requirements.mp4 16.3 MB
  • 8. Scikit-Optimize/2. Section content.mp4 13.1 MB
  • 8. Scikit-Optimize/5. Defining the objective function.mp4 11.1 MB
  • 1. Introduction/4. Course material.mp4 10.6 MB
  • 9. Hyperopt/2. Section content.mp4 7.2 MB
  • 3. Performance metrics/1. Performance Metrics - Introduction.mp4 6.1 MB
  • 10. Optuna/3. Section content.mp4 4.2 MB
  • 6. Bayesian Optimization/6. Sequential Model-Based Optimization-en_US.srt 19.0 kB
  • 6. Bayesian Optimization/8. Multivariate Gaussian Distribution-en_US.srt 18.4 kB
  • 6. Bayesian Optimization/12. Scikit-Optimize - 1-Dimension-en_US.srt 17.9 kB
  • 6. Bayesian Optimization/16. Scikit-Optimize - Neuronal Networks-en_US.srt 17.5 kB
  • 8. Scikit-Optimize/13. Optimizing Hyperparameters of a CNN-en_US.srt 17.5 kB
  • 9. Hyperopt/3. Search space configuration and distributions-en_US.srt 16.3 kB
  • 4. Cross-Validation/2. Cross-Validation schemes-en_US.srt 16.1 kB
  • 6. Bayesian Optimization/9. Gaussian Process-en_US.srt 15.3 kB
  • 6. Bayesian Optimization/11. Acquisition Functions-en_US.srt 15.2 kB
  • 6. Bayesian Optimization/5. Bayes Rule-en_US.srt 13.4 kB
  • 7. Other SMBO Algorithms/2. SMAC Demo-en_US.srt 13.2 kB
  • 2. Hyperparameter Tuning - Overview/1. Parameters and Hyperparameters-en_US.srt 13.1 kB
  • 5. Basic Search Algorithms/9. Random Search with Hyperopt-en_US.srt 12.5 kB
  • 10. Optuna/6. Optimizing hyperparameters of a CNN-en_US.srt 11.2 kB
  • 10. Optuna/8. Evaluating the search with Optuna's built in functions-en_US.srt 11.1 kB
  • 4. Cross-Validation/1. Cross-Validation-en_US.srt 10.9 kB
  • 9. Hyperopt/7. Optimizing multiple ML models simultaneously-en_US.srt 10.8 kB
  • 3. Performance metrics/5. Creating your own metrics-en_US.srt 10.6 kB
  • 2. Hyperparameter Tuning - Overview/2. Hyperparameter Optimization-en_US.srt 10.3 kB
  • 4. Cross-Validation/3. Estimating the model generalization error with CV - Demo-en_US.srt 10.2 kB
  • 9. Hyperopt/6. Evaluating the search-en_US.srt 9.9 kB
  • 5. Basic Search Algorithms/4. Grid Search - Demo-en_US.srt 9.7 kB
  • 9. Hyperopt/1. Hyperopt-en_US.srt 9.6 kB
  • 4. Cross-Validation/4. Cross-Validation for Hyperparameter Tuning - Demo-en_US.srt 9.3 kB
  • 5. Basic Search Algorithms/8. Random Search with Scikit-Optimize-en_US.srt 9.3 kB
  • 9. Hyperopt/5. Search algorithms-en_US.srt 9.3 kB
  • 3. Performance metrics/2. Classification Metrics (Optional)-en_US.srt 9.2 kB
  • 5. Basic Search Algorithms/6. Random Search-en_US.srt 9.1 kB
  • 7. Other SMBO Algorithms/4. TPE Procedure-en_US.srt 9.0 kB
  • 6. Bayesian Optimization/3. Bayesian Inference - Introduction-en_US.srt 8.8 kB
  • 5. Basic Search Algorithms/2. Manual Search-en_US.srt 8.7 kB
  • 6. Bayesian Optimization/4. Joint and Conditional Probabilities-en_US.srt 8.7 kB
  • 4. Cross-Validation/7. Nested Cross-Validation-en_US.srt 8.6 kB
  • 10. Optuna/4. Search algorithms-en_US.srt 8.5 kB
  • 10. Optuna/5. Optimizing multiple ML models with simultaneously-en_US.srt 8.5 kB
  • 10. Optuna/2. Optuna main functions-en_US.srt 8.3 kB
  • 6. Bayesian Optimization/7. Gaussian Distribution-en_US.srt 8.3 kB
  • 4. Cross-Validation/5. Special Cross-Validation schemes-en_US.srt 8.3 kB
  • 4. Cross-Validation/8. Nested Cross-Validation - Demo-en_US.srt 8.2 kB
  • 3. Performance metrics/4. Scikit-learn metrics-en_US.srt 7.6 kB
  • 6. Bayesian Optimization/17. Scikit-Optimize - CNN - Search Analysis-en_US.srt 7.5 kB
  • 8. Scikit-Optimize/14. Analyzing the CNN search-en_US.srt 7.5 kB
  • 1. Introduction/2. Course curriculum-en_US.srt 7.5 kB
  • 6. Bayesian Optimization/10. Kernels-en_US.srt 7.5 kB
  • 7. Other SMBO Algorithms/7. TPE with Hyperopt-en_US.srt 7.4 kB
  • 7. Other SMBO Algorithms/1. SMAC-en_US.srt 7.0 kB
  • 6. Bayesian Optimization/13. Scikit-Optimize - Manual Search-en_US.srt 6.9 kB
  • 6. Bayesian Optimization/1. Sequential Search-en_US.srt 6.7 kB
  • 8. Scikit-Optimize/1. Scikit-Optimize-en_US.srt 6.7 kB
  • 5. Basic Search Algorithms/7. Random Search with Scikit-learn-en_US.srt 6.5 kB
  • 8. Scikit-Optimize/7. Bayesian search with Gaussian processes-en_US.srt 6.4 kB
  • 5. Basic Search Algorithms/1. Basic Search Algorithms - Introduction-en_US.srt 6.3 kB
  • 8. Scikit-Optimize/6. Random search-en_US.srt 6.1 kB
  • 4. Cross-Validation/6. Group Cross-Validation - Demo-en_US.srt 6.0 kB
  • 10. Optuna/7. Optimizing a CNN - extended-en_US.srt 5.5 kB
  • 6. Bayesian Optimization/2. Bayesian Optimization-en_US.srt 5.4 kB
  • 10. Optuna/1. Optuna-en_US.srt 5.3 kB
  • 6. Bayesian Optimization/14. Scikit-Optimize - Automatic Search-en_US.srt 5.2 kB
  • 8. Scikit-Optimize/11. Bayesian search with Scikit-learn wrapper-en_US.srt 5.2 kB
  • 7. Other SMBO Algorithms/5. TPE hyperparameters-en_US.srt 5.0 kB
  • 8. Scikit-Optimize/3. Hyperparameter Distributions-en_US.srt 4.9 kB
  • 7. Other SMBO Algorithms/6. TPE - why tree-structured-en_US.srt 4.6 kB
  • 9. Hyperopt/4. Sampling from nested spaces-en_US.srt 4.5 kB
  • 6. Bayesian Optimization/15. Scikit-Optimize - Alternative Kernel-en_US.srt 4.3 kB
  • 8. Scikit-Optimize/12. Changing the kernel of a Gaussian Process-en_US.srt 4.3 kB
  • 5. Basic Search Algorithms/3. Grid Search-en_US.srt 4.3 kB
  • 1. Introduction/1. Introduction-en_US.srt 4.1 kB
  • 7. Other SMBO Algorithms/3. Tree-structured Parzen Estimators - TPE-en_US.srt 4.1 kB
  • 3. Performance metrics/3. Regression Metrics (Optional)-en_US.srt 3.9 kB
  • 8. Scikit-Optimize/8. Bayesian search with Random Forests-en_US.srt 3.6 kB
  • 8. Scikit-Optimize/9. Bayesian search with GBMs-en_US.srt 3.5 kB
  • 8. Scikit-Optimize/10. Parallelizing a Bayesian search-en_US.srt 3.2 kB
  • 1. Introduction/FAQ.html 3.0 kB
  • 8. Scikit-Optimize/4. Defining the hyperparameter space-en_US.srt 2.9 kB
  • 1. Introduction/3. Course aim and knowledge requirements-en_US.srt 2.8 kB
  • 5. Basic Search Algorithms/5. Grid Search with different hyperparameter spaces-en_US.srt 2.8 kB
  • 8. Scikit-Optimize/2. Section content-en_US.srt 2.7 kB
  • 8. Scikit-Optimize/5. Defining the objective function-en_US.srt 2.4 kB
  • 3. Performance metrics/6. Using Scikit-learn metrics-en_US.srt 2.3 kB
  • 9. Hyperopt/2. Section content-en_US.srt 2.2 kB
  • 1. Introduction/4. Course material-en_US.srt 2.2 kB
  • 3. Performance metrics/1. Performance Metrics - Introduction-en_US.srt 1.4 kB
  • 6. Bayesian Optimization/Additional Reading Resources.html 1.4 kB
  • 10. Optuna/3. Section content-en_US.srt 1.1 kB
  • 1. Introduction/Jupyter notebooks.html 931 Bytes
  • 1. Introduction/Set up your computer - required packages.html 736 Bytes
  • 11. Moving Forward/What's next.html 707 Bytes
  • 1. Introduction/Datasets.html 598 Bytes
  • 9. Hyperopt/Optimizing Hyperparameters of a CNN.html 448 Bytes
  • 9. Hyperopt/References.html 424 Bytes
  • 8. Scikit-Optimize/Optimizing xgboost.html 371 Bytes
  • 1. Introduction/Presentations.html 286 Bytes
  • 4. Cross-Validation/Bias vs Variance (Optional).html 196 Bytes
  • 10. Optuna/References.html 181 Bytes
  • 0. Websites you may like/[Tutorialsplanet.NET].url 128 Bytes
  • 2. Hyperparameter Tuning - Overview/[Tutorialsplanet.NET].url 128 Bytes
  • 5. Basic Search Algorithms/[Tutorialsplanet.NET].url 128 Bytes
  • 9. Hyperopt/[Tutorialsplanet.NET].url 128 Bytes
  • [Tutorialsplanet.NET].url 128 Bytes

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