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Hyperparameter Optimization for Machine Learning
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[TutsNode.com] - Hyperparameter Optimization for Machine Learning (Size: 2.92 GB) (Files: 211)
[TutsNode.com] - Hyperparameter Optimization for Machine Learning
06 Bayesian Optimization
006 Sequential Model-Based Optimization.mp4
006 Sequential Model-Based Optimization.en.srt
008 Multivariate Gaussian Distribution.en.srt
013 Scikit-Optimize - 1-Dimension.en.srt
017 Scikit-Optimize - Neuronal Networks.en.srt
009 Gaussian Process.en.srt
011 Acquisition Functions.en.srt
005 Bayes Rule.en.srt
003 Bayesian Inference - Introduction.en.srt
004 Joint and Conditional Probabilities.en.srt
007 Gaussian Distribution.en.srt
018 Scikit-Optimize - CNN - Search Analysis.en.srt
010 Kernels.en.srt
014 Scikit-Optimize - Manual Search.en.srt
001 Sequential Search.en.srt
002 Bayesian Optimization.en.srt
015 Scikit-Optimize - Automatic Search.en.srt
016 Scikit-Optimize - Alternative Kernel.en.srt
012 Additional Reading Resources.html
017 Scikit-Optimize - Neuronal Networks.mp4
013 Scikit-Optimize - 1-Dimension.mp4
008 Multivariate Gaussian Distribution.mp4
011 Acquisition Functions.mp4
009 Gaussian Process.mp4
005 Bayes Rule.mp4
004 Joint and Conditional Probabilities.mp4
003 Bayesian Inference - Introduction.mp4
018 Scikit-Optimize - CNN - Search Analysis.mp4
014 Scikit-Optimize - Manual Search.mp4
007 Gaussian Distribution.mp4
015 Scikit-Optimize - Automatic Search.mp4
001 Sequential Search.mp4
010 Kernels.mp4
016 Scikit-Optimize - Alternative Kernel.mp4
002 Bayesian Optimization.mp4
01 Introduction
003 Course aim and knowledge requirements.en.srt
004 Course Material.en.srt
005 Jupyter notebooks.html
006 Presentations.html
007 Datasets.html
008 Set up your computer - required packages.html
002 Course Curriculum.en.srt
001 Introduction.en.srt
009 FAQ.html
001 Introduction.mp4
002 Course Curriculum.mp4
003 Course aim and knowledge requirements.mp4
004 Course Material.mp4
08 Scikit-Optimize
014 Optimizing parameters of a CNN.en.srt
015 Analyzing the CNN search.en.srt
001 Scikit-Optimize.en.srt
007 Bayesian search with Gaussian processes.en.srt
006 Random search.en.srt
011 Bayesian search with Scikit-learn wrapper.en.srt
003 Hyperparameter Distributions.en.srt
012 Changing the kernel of a Gaussian Process.en.srt
008 Bayes search with Random Forests.en.srt
009 Bayes search with GBMs.en.srt
010 Parallelizing a bayesian search.en.srt
004 Defining the hyperparameter space.en.srt
002 Section Content.en.srt
005 Defining the objective function.en.srt
013 Optimizing xgboost.html
014 Optimizing parameters of a CNN.mp4
006 Random search.mp4
015 Analyzing the CNN search.mp4
007 Bayesian search with Gaussian processes.mp4
011 Bayesian search with Scikit-learn wrapper.mp4
010 Parallelizing a bayesian search.mp4
012 Changing the kernel of a Gaussian Process.mp4
001 Scikit-Optimize.mp4
003 Hyperparameter Distributions.mp4
009 Bayes search with GBMs.mp4
008 Bayes search with Random Forests.mp4
004 Defining the hyperparameter space.mp4
002 Section Content.mp4
005 Defining the objective function.mp4
04 Cross-Validation
003 Cross-Validation Schemes.en.srt
001 Cross-Validation.en.srt
004 Cross-Validation for model error estimation - Demo.en.srt
005 Cross-Validation for Hyperparameter Tuning - Demo.en.srt
002 Bias vs Variance (Optional).html
008 Nested Cross-Validation.en.srt
006 Special Cross-Validation Schemes.en.srt
009 Nested Cross-Validation - Demo.en.srt
007 Group Cross-Validation - Demo.en.srt
003 Cross-Validation Schemes.mp4
004 Cross-Validation for model error estimation - Demo.mp4
001 Cross-Validation.mp4
005 Cross-Validation for Hyperparameter Tuning - Demo.mp4
009 Nested Cross-Validation - Demo.mp4
008 Nested Cross-Validation.mp4
007 Group Cross-Validation - Demo.mp4
006 Special Cross-Validation Schemes.mp4
07 Other SMBO Algorithms
002 SMAC Demo.en.srt
004 TPE Procedure.en.srt
007 TPE with Hyperopt.en.srt
001 SMAC.en.srt
005 TPE hyperparameters.en.srt
006 TPE - why tree-structured_.en.srt
003 Tree-structured Parzen Estimators - TPE.en.srt
002 SMAC Demo.mp4
007 TPE with Hyperopt.mp4
004 TPE Procedure.mp4
001 SMAC.mp4
006 TPE - why tree-structured_.mp4
005 TPE hyperparameters.mp4
003 Tree-structured Parzen Estimators - TPE.mp4
02 Hyperparameter Tuning - Overview
001 Parameters and Hyperparameters.en.srt
002 Hyperparameter Optimization.en.srt
001 Parameters and Hyperparameters.mp4
002 Hyperparameter Optimization.mp4
03 Performance metrics
001 Introduction.en.srt
005 Creating your Own Metrics.en.srt
002 Classification Metrics (Optional).en.srt
006 Using Scikit-learn Metrics.en.srt
004 Scikit-learn Metrics.en.srt
003 Regression Metrics (Optional).en.srt
005 Creating your Own Metrics.mp4
004 Scikit-learn Metrics.mp4
002 Classification Metrics (Optional).mp4
006 Using Scikit-learn Metrics.mp4
003 Regression Metrics (Optional).mp4
001 Introduction.mp4
05 Basic Search Algorithms
009 Random Search with Hyperopt.en.srt
004 Grid Search - Demo.en.srt
008 Random Search with Scikit-Optimize.en.srt
006 Random Search.en.srt
002 Manual Search.en.srt
007 Random Search - Scikit-learn.en.srt
001 Basic Search Algorithms - Introduction.en.srt
005 Grid Search with different hyperparameter spaces.en.srt
003 Grid Search.en.srt
009 Random Search with Hyperopt.mp4
004 Grid Search - Demo.mp4
008 Random Search with Scikit-Optimize.mp4
007 Random Search - Scikit-learn.mp4
002 Manual Search.mp4
006 Random Search.mp4
001 Basic Search Algorithms - Introduction.mp4
005 Grid Search with different hyperparameter spaces.mp4
003 Grid Search.mp4
09 Moving Forward
001 What's next_.html
TutsNode.com.txt
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