Udemy - Hyperparameter Optimization for Machine Learning (9.2024)

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Udemy - Hyperparameter Optimization for Machine Learning (9.2024)

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种子哈希：95c51fe4bc58c931ef1ed39c52f8fb4c8dc44191
文件大小： 4.15G
已经下载：3次
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收录时间：2025-12-20
最近下载：2025-12-20

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文件列表

  • 06 - Bayesian Optimization/050 Scikit-Optimize - Neuronal Networks.mp4 179.9 MB
  • 08 - Scikit-Optimize/074 Optimizing Hyperparameters of a CNN.mp4 179.9 MB
  • 07 - Other SMBO Algorithms/054 SMAC Demo.mp4 160.7 MB
  • 06 - Bayesian Optimization/046 Scikit-Optimize - 1-Dimension.mp4 142.4 MB
  • 10 - Optuna/090 Optimizing hyperparameters of a CNN.mp4 137.4 MB
  • 04 - Cross-Validation/019 Cross-Validation schemes.mp4 135.4 MB
  • 09 - Hyperopt/078 Search space configuration and distributions.mp4 127.2 MB
  • 06 - Bayesian Optimization/041 Multivariate Gaussian Distribution.mp4 121.1 MB
  • 06 - Bayesian Optimization/039 Sequential Model-Based Optimization.mp4 118.4 MB
  • 04 - Cross-Validation/020 Estimating the model generalization error with CV - Demo.mp4 117.0 MB
  • 03 - Performance metrics/015 Creating your own metrics.mp4 115.3 MB
  • 06 - Bayesian Optimization/044 Acquisition Functions.mp4 112.9 MB
  • 09 - Hyperopt/080 Search algorithms.mp4 103.3 MB
  • 10 - Optuna/092 Evaluating the search with Optuna's built in functions.mp4 99.7 MB
  • 10 - Optuna/088 Search algorithms.mp4 89.5 MB
  • 05 - Basic Search Algorithms/030 Random Search with Scikit-learn.mp4 87.5 MB
  • 04 - Cross-Validation/023 Group Cross-Validation - Demo.mp4 87.4 MB
  • 04 - Cross-Validation/017 Cross-Validation.mp4 84.3 MB
  • 05 - Basic Search Algorithms/025 Manual Search.mp4 82.5 MB
  • 10 - Optuna/089 Optimizing multiple ML models with simultaneously.mp4 81.9 MB
  • 09 - Hyperopt/082 Optimizing multiple ML models simultaneously.mp4 81.8 MB
  • 04 - Cross-Validation/021 Cross-Validation for Hyperparameter Tuning - Demo.mp4 80.8 MB
  • 07 - Other SMBO Algorithms/059 TPE with Hyperopt.mp4 80.2 MB
  • 05 - Basic Search Algorithms/031 Random Search with Scikit-Optimize.mp4 76.5 MB
  • 09 - Hyperopt/081 Evaluating the search.mp4 68.2 MB
  • 06 - Bayesian Optimization/042 Gaussian Process.mp4 61.3 MB
  • 07 - Other SMBO Algorithms/060 Discussion Bayesian Optimization and Basic Search.mp4 61.2 MB
  • 05 - Basic Search Algorithms/029 Random Search.mp4 60.2 MB
  • 06 - Bayesian Optimization/038 Bayes Rule.mp4 60.1 MB
  • 08 - Scikit-Optimize/066 Random search.mp4 58.9 MB
  • 10 - Optuna/091 Optimizing a CNN - extended.mp4 53.6 MB
  • 06 - Bayesian Optimization/047 Scikit-Optimize - Manual Search.mp4 53.1 MB
  • 08 - Scikit-Optimize/067 Bayesian search with Gaussian processes.mp4 52.5 MB
  • 06 - Bayesian Optimization/051 Scikit-Optimize - CNN - Search Analysis.mp4 49.2 MB
  • 05 - Basic Search Algorithms/027 Grid Search - Demo.mp4 48.5 MB
  • 05 - Basic Search Algorithms/032 Random Search with Hyperopt.mp4 47.1 MB
  • 06 - Bayesian Optimization/036 Bayesian Inference - Introduction.mp4 46.0 MB
  • 08 - Scikit-Optimize/070 Parallelizing a Bayesian search.mp4 42.0 MB
  • 02 - Hyperparameter Tuning - Overview/010 Parameters and Hyperparameters.mp4 39.9 MB
  • 02 - Hyperparameter Tuning - Overview/011 Hyperparameter Optimization.mp4 38.8 MB
  • 06 - Bayesian Optimization/048 Scikit-Optimize - Automatic Search.mp4 38.0 MB
  • 08 - Scikit-Optimize/071 Bayesian search with Scikit-learn wrapper.mp4 38.0 MB
  • 05 - Basic Search Algorithms/024 Basic Search Algorithms - Introduction.mp4 37.9 MB
  • 06 - Bayesian Optimization/035 Bayesian Optimization.mp4 37.2 MB
  • 05 - Basic Search Algorithms/028 Grid Search with different hyperparameter spaces.mp4 36.5 MB
  • 03 - Performance metrics/016 Using Scikit-learn metrics.mp4 35.7 MB
  • 08 - Scikit-Optimize/069 Bayesian search with GBMs.mp4 35.0 MB
  • 10 - Optuna/086 Optuna main functions.mp4 34.3 MB
  • 06 - Bayesian Optimization/034 Sequential Search.mp4 33.4 MB
  • 01 - Introduction/001 Introduction.mp4 32.3 MB
  • 08 - Scikit-Optimize/068 Bayesian search with Random Forests.mp4 32.1 MB
  • 06 - Bayesian Optimization/037 Joint and Conditional Probabilities.mp4 31.9 MB
  • 08 - Scikit-Optimize/072 Changing the kernel of a Gaussian Process.mp4 30.7 MB
  • 06 - Bayesian Optimization/049 Scikit-Optimize - Alternative Kernel.mp4 30.7 MB
  • 03 - Performance metrics/013 Classification Metrics (Optional).mp4 30.2 MB
  • 08 - Scikit-Optimize/063 Hyperparameter Distributions.mp4 29.1 MB
  • 04 - Cross-Validation/022 Special Cross-Validation schemes.mp4 28.3 MB
  • 07 - Other SMBO Algorithms/056 TPE Procedure.mp4 28.0 MB
  • 07 - Other SMBO Algorithms/053 SMAC.mp4 26.8 MB
  • 09 - Hyperopt/076 Hyperopt.mp4 24.7 MB
  • 06 - Bayesian Optimization/040 Gaussian Distribution.mp4 23.5 MB
  • 07 - Other SMBO Algorithms/058 TPE - why tree-structured.mp4 21.4 MB
  • 08 - Scikit-Optimize/075 Analyzing the CNN search.mp4 20.9 MB
  • 06 - Bayesian Optimization/043 Kernels.mp4 20.4 MB
  • 09 - Hyperopt/079 Sampling from nested spaces.mp4 17.6 MB
  • 08 - Scikit-Optimize/061 Scikit-Optimize.mp4 17.0 MB
  • 07 - Other SMBO Algorithms/057 TPE hyperparameters.mp4 16.1 MB
  • 05 - Basic Search Algorithms/026 Grid Search.mp4 15.4 MB
  • 10 - Optuna/085 Optuna.mp4 15.0 MB
  • 07 - Other SMBO Algorithms/055 Tree-structured Parzen Estimators - TPE.mp4 13.2 MB
  • 01 - Introduction/002 Course curriculum.mp4 13.1 MB
  • 07 - Other SMBO Algorithms/052 Other SMBO Algorithms.mp4 12.5 MB
  • 01 - Introduction/003 Course aim and knowledge requirements.mp4 12.4 MB
  • 03 - Performance metrics/014 Regression Metrics (Optional).mp4 12.1 MB
  • 08 - Scikit-Optimize/064 Defining the hyperparameter space.mp4 10.6 MB
  • 08 - Scikit-Optimize/062 Section content.mp4 8.6 MB
  • 08 - Scikit-Optimize/065 Defining the objective function.mp4 7.2 MB
  • 01 - Introduction/004 Course material.mp4 6.7 MB
  • 09 - Hyperopt/077 Section content.mp4 5.0 MB
  • 03 - Performance metrics/012 Performance Metrics - Introduction.mp4 4.3 MB
  • 10 - Optuna/087 Section content.mp4 3.0 MB
  • 06 - Bayesian Optimization/041 Multivariate Gaussian Distribution.srt 23.5 kB
  • 06 - Bayesian Optimization/039 Sequential Model-Based Optimization.srt 23.2 kB
  • 06 - Bayesian Optimization/046 Scikit-Optimize - 1-Dimension.srt 22.6 kB
  • 06 - Bayesian Optimization/050 Scikit-Optimize - Neuronal Networks.srt 21.2 kB
  • 08 - Scikit-Optimize/074 Optimizing Hyperparameters of a CNN.srt 21.2 kB
  • 04 - Cross-Validation/019 Cross-Validation schemes.srt 21.0 kB
  • 09 - Hyperopt/078 Search space configuration and distributions.srt 20.4 kB
  • 06 - Bayesian Optimization/044 Acquisition Functions.srt 19.3 kB
  • 06 - Bayesian Optimization/042 Gaussian Process.srt 18.8 kB
  • 07 - Other SMBO Algorithms/060 Discussion Bayesian Optimization and Basic Search.srt 18.2 kB
  • 06 - Bayesian Optimization/038 Bayes Rule.srt 16.9 kB
  • 07 - Other SMBO Algorithms/054 SMAC Demo.srt 16.5 kB
  • 02 - Hyperparameter Tuning - Overview/010 Parameters and Hyperparameters.srt 16.1 kB
  • 05 - Basic Search Algorithms/032 Random Search with Hyperopt.srt 15.7 kB
  • 10 - Optuna/092 Evaluating the search with Optuna's built in functions.srt 14.2 kB
  • 04 - Cross-Validation/017 Cross-Validation.srt 14.0 kB
  • 10 - Optuna/090 Optimizing hyperparameters of a CNN.srt 13.8 kB
  • 09 - Hyperopt/082 Optimizing multiple ML models simultaneously.srt 13.5 kB
  • 03 - Performance metrics/015 Creating your own metrics.srt 13.0 kB
  • 02 - Hyperparameter Tuning - Overview/011 Hyperparameter Optimization.srt 12.8 kB
  • 04 - Cross-Validation/020 Estimating the model generalization error with CV - Demo.srt 12.8 kB
  • 09 - Hyperopt/081 Evaluating the search.srt 12.7 kB
  • 05 - Basic Search Algorithms/027 Grid Search - Demo.srt 11.9 kB
  • 09 - Hyperopt/076 Hyperopt.srt 11.9 kB
  • 05 - Basic Search Algorithms/031 Random Search with Scikit-Optimize.srt 11.8 kB
  • 04 - Cross-Validation/021 Cross-Validation for Hyperparameter Tuning - Demo.srt 11.7 kB
  • 05 - Basic Search Algorithms/029 Random Search.srt 11.6 kB
  • 09 - Hyperopt/080 Search algorithms.srt 11.4 kB
  • 03 - Performance metrics/013 Classification Metrics (Optional).srt 11.3 kB
  • 06 - Bayesian Optimization/037 Joint and Conditional Probabilities.srt 11.0 kB
  • 07 - Other SMBO Algorithms/056 TPE Procedure.srt 11.0 kB
  • 06 - Bayesian Optimization/036 Bayesian Inference - Introduction.srt 10.9 kB
  • 06 - Bayesian Optimization/040 Gaussian Distribution.srt 10.5 kB
  • 10 - Optuna/089 Optimizing multiple ML models with simultaneously.srt 10.5 kB
  • 05 - Basic Search Algorithms/025 Manual Search.srt 10.4 kB
  • 10 - Optuna/088 Search algorithms.srt 10.3 kB
  • 04 - Cross-Validation/022 Special Cross-Validation schemes.srt 10.3 kB
  • 10 - Optuna/086 Optuna main functions.srt 10.2 kB
  • 01 - Introduction/002 Course curriculum.srt 9.4 kB
  • 06 - Bayesian Optimization/051 Scikit-Optimize - CNN - Search Analysis.srt 9.3 kB
  • 08 - Scikit-Optimize/075 Analyzing the CNN search.srt 9.3 kB
  • 07 - Other SMBO Algorithms/053 SMAC.srt 9.1 kB
  • 06 - Bayesian Optimization/043 Kernels.srt 9.1 kB
  • 07 - Other SMBO Algorithms/059 TPE with Hyperopt.srt 9.0 kB
  • 06 - Bayesian Optimization/047 Scikit-Optimize - Manual Search.srt 8.8 kB
  • 08 - Scikit-Optimize/061 Scikit-Optimize.srt 8.4 kB
  • 06 - Bayesian Optimization/034 Sequential Search.srt 8.3 kB
  • 05 - Basic Search Algorithms/030 Random Search with Scikit-learn.srt 8.1 kB
  • 05 - Basic Search Algorithms/024 Basic Search Algorithms - Introduction.srt 7.9 kB
  • 08 - Scikit-Optimize/066 Random search.srt 7.9 kB
  • 08 - Scikit-Optimize/067 Bayesian search with Gaussian processes.srt 7.9 kB
  • 04 - Cross-Validation/023 Group Cross-Validation - Demo.srt 7.7 kB
  • 06 - Bayesian Optimization/035 Bayesian Optimization.srt 7.2 kB
  • 10 - Optuna/091 Optimizing a CNN - extended.srt 6.7 kB
  • 06 - Bayesian Optimization/048 Scikit-Optimize - Automatic Search.srt 6.6 kB
  • 08 - Scikit-Optimize/071 Bayesian search with Scikit-learn wrapper.srt 6.6 kB
  • 10 - Optuna/085 Optuna.srt 6.6 kB
  • 07 - Other SMBO Algorithms/057 TPE hyperparameters.srt 6.4 kB
  • 08 - Scikit-Optimize/063 Hyperparameter Distributions.srt 6.4 kB
  • 07 - Other SMBO Algorithms/052 Other SMBO Algorithms.srt 6.1 kB
  • 07 - Other SMBO Algorithms/058 TPE - why tree-structured.srt 5.8 kB
  • 09 - Hyperopt/079 Sampling from nested spaces.srt 5.5 kB
  • 06 - Bayesian Optimization/049 Scikit-Optimize - Alternative Kernel.srt 5.4 kB
  • 08 - Scikit-Optimize/072 Changing the kernel of a Gaussian Process.srt 5.4 kB
  • 07 - Other SMBO Algorithms/055 Tree-structured Parzen Estimators - TPE.srt 5.3 kB
  • 05 - Basic Search Algorithms/026 Grid Search.srt 5.3 kB
  • 01 - Introduction/001 Introduction.srt 5.1 kB
  • 03 - Performance metrics/014 Regression Metrics (Optional).srt 5.0 kB
  • 01 - Introduction/009 Resources to learn machine learning skills.html 5.0 kB
  • 08 - Scikit-Optimize/069 Bayesian search with GBMs.srt 4.6 kB
  • 08 - Scikit-Optimize/068 Bayesian search with Random Forests.srt 4.4 kB
  • 08 - Scikit-Optimize/070 Parallelizing a Bayesian search.srt 4.1 kB
  • 11 - Final bonus section/095 Bonus lecture.html 3.9 kB
  • 01 - Introduction/003 Course aim and knowledge requirements.srt 3.7 kB
  • 06 - Bayesian Optimization/045 Additional Reading Resources.html 3.6 kB
  • 08 - Scikit-Optimize/064 Defining the hyperparameter space.srt 3.6 kB
  • 01 - Introduction/005 Jupyter notebooks.html 3.6 kB
  • 05 - Basic Search Algorithms/028 Grid Search with different hyperparameter spaces.srt 3.5 kB
  • 08 - Scikit-Optimize/062 Section content.srt 3.3 kB
  • 01 - Introduction/008 Set up your computer - required packages.html 3.0 kB
  • 05 - Basic Search Algorithms/033 More examples.html 3.0 kB
  • 08 - Scikit-Optimize/065 Defining the objective function.srt 3.0 kB
  • 01 - Introduction/006 Presentations.html 2.9 kB
  • 03 - Performance metrics/016 Using Scikit-learn metrics.srt 2.8 kB
  • 01 - Introduction/007 Datasets.html 2.8 kB
  • 09 - Hyperopt/083 Optimizing Hyperparameters of a CNN.html 2.7 kB
  • 01 - Introduction/004 Course material.srt 2.7 kB
  • 09 - Hyperopt/084 References.html 2.6 kB
  • 08 - Scikit-Optimize/073 Optimizing xgboost.html 2.6 kB
  • 09 - Hyperopt/077 Section content.srt 2.5 kB
  • 10 - Optuna/094 More examples.html 2.5 kB
  • 04 - Cross-Validation/018 Bias vs Variance (Optional).html 2.4 kB
  • 10 - Optuna/093 References.html 2.4 kB
  • 03 - Performance metrics/012 Performance Metrics - Introduction.srt 1.7 kB
  • 10 - Optuna/087 Section content.srt 1.3 kB

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