| 0 | 357.4 KB | ||
| 001 Basic Search Algorithms - Introduction.en.srt | 6.6 KB | ||
| 001 Basic Search Algorithms - Introduction.mp4 | 25.5 MB | ||
| 001 Cross-Validation.en.srt | 11.4 KB | ||
| 001 Cross-Validation.mp4 | 57.7 MB | ||
| 001 Introduction.en.srt | 1.5 KB | ||
| 001 Introduction.mp4 | 5.8 MB | ||
| 001 Parameters and Hyperparameters.en.srt | 13.7 KB | ||
| 001 Parameters and Hyperparameters.mp4 | 62.3 MB | ||
| 001 SMAC.en.srt | 7.4 KB | ||
| 001 SMAC.mp4 | 32.6 MB | ||
| 1 | 640.7 KB | ||
| 001 Scikit-Optimize.en.srt | 7 KB | ||
| 001 Scikit-Optimize.mp4 | 24.8 MB | ||
| 001 Sequential Search.en.srt | 7.1 KB | ||
| 001 Sequential Search.mp4 | 30.6 MB | ||
| 001 What's next_.html | 1.6 KB | ||
| 002 Bayesian Optimization.en.srt | 5.7 KB | ||
| 002 Bias vs Variance (Optional).html | 1.1 KB | ||
| 002 Classification Metrics (Optional).en.srt | 9.6 KB | ||
| 002 Course Curriculum.mp4 | 34.9 MB | ||
| 2 | 670.6 KB | ||
| 002 Bayesian Optimization.mp4 | 22.4 MB | ||
| 002 Classification Metrics (Optional).mp4 | 42.9 MB | ||
| 002 Course Curriculum.en.srt | 7.9 KB | ||
| 002 Hyperparameter Optimization.en.srt | 10.8 KB | ||
| 002 Hyperparameter Optimization.mp4 | 50.8 MB | ||
| 002 Manual Search.en.srt | 9.1 KB | ||
| 002 Manual Search.mp4 | 43.1 MB | ||
| 002 SMAC Demo.en.srt | 13.9 KB | ||
| 002 SMAC Demo.mp4 | 99.6 MB | ||
| 002 Section Content.en.srt | 2.8 KB | ||
| 002 Section Content.mp4 | 12.5 MB | ||
| 003 Course aim and knowledge requirements.mp4 | 15.5 MB | ||
| 003 Cross-Validation Schemes.en.srt | 16.8 KB | ||
| 003 Cross-Validation Schemes.mp4 | 79.8 MB | ||
| 003 Grid Search.mp4 | 16.3 MB | ||
| 003 Hyperparameter Distributions.en.srt | 5.2 KB | ||
| 003 Regression Metrics (Optional).en.srt | 4.1 KB | ||
| 3 | 450.3 KB | ||
| 003 Bayesian Inference - Introduction.en.srt | 9.3 KB | ||
| 003 Bayesian Inference - Introduction.mp4 | 43.4 MB | ||
| 003 Course aim and knowledge requirements.en.srt | 2.9 KB | ||
| 003 Grid Search.en.srt | 4.5 KB | ||
| 003 Hyperparameter Distributions.mp4 | 24.1 MB | ||
| 003 Regression Metrics (Optional).mp4 | 16.6 MB | ||
| 003 Tree-structured Parzen Estimators - TPE.en.srt | 4.2 KB | ||
| 003 Tree-structured Parzen Estimators - TPE.mp4 | 19.3 MB | ||
| 004 Course Material.en.srt | 2.3 KB | ||
| 4 | 798.3 KB | ||
| 004 Course Material.mp4 | 10.1 MB | ||
| 004 Cross-Validation for model error estimation - Demo.en.srt | 10.7 KB | ||
| 004 Cross-Validation for model error estimation - Demo.mp4 | 65.8 MB | ||
| 004 Defining the hyperparameter space.en.srt | 3 KB | ||
| 004 Defining the hyperparameter space.mp4 | 17.2 MB | ||
| 004 Grid Search - Demo.en.srt | 10.3 KB | ||
| 004 Grid Search - Demo.mp4 | 59.4 MB | ||
| 004 Joint and Conditional Probabilities.en.srt | 9.1 KB | ||
| 004 Joint and Conditional Probabilities.mp4 | 46.2 MB | ||
| 004 Scikit-learn Metrics.en.srt | 8 KB | ||
| 004 Scikit-learn Metrics.mp4 | 45.9 MB | ||
| 004 TPE Procedure.en.srt | 9.4 KB | ||
| 004 TPE Procedure.mp4 | 42.3 MB | ||
| 005 Bayes Rule.en.srt | 14 KB | ||
| 5 | 95 KB | ||
| 005 Bayes Rule.mp4 | 67.8 MB | ||
| 005 Creating your Own Metrics.en.srt | 11.2 KB | ||
| 005 Creating your Own Metrics.mp4 | 64.5 MB | ||
| 005 Cross-Validation for Hyperparameter Tuning - Demo.en.srt | 9.8 KB | ||
| 005 Cross-Validation for Hyperparameter Tuning - Demo.mp4 | 56.8 MB | ||
| 005 Defining the objective function.en.srt | 2.5 KB | ||
| 005 Defining the objective function.mp4 | 10.6 MB | ||
| 005 Grid Search with different hyperparameter spaces.en.srt | 2.9 KB | ||
| 005 Grid Search with different hyperparameter spaces.mp4 | 18.4 MB | ||
| 005 Jupyter notebooks.html | 1.8 KB | ||
| 005 TPE hyperparameters.en.srt | 5.2 KB | ||
| 005 TPE hyperparameters.mp4 | 23.2 MB | ||
| 6 | 696.1 KB | ||
| 006 Presentations.html | 1.1 KB | ||
| 006 Random Search.en.srt | 9.5 KB | ||
| 006 Random Search.mp4 | 41 MB | ||
| 006 Random search.en.srt | 6.4 KB | ||
| 006 Random search.mp4 | 38.2 MB | ||
| 006 Sequential Model-Based Optimization.en.srt | 20 KB | ||
| 006 Sequential Model-Based Optimization.mp4 | 114.1 MB | ||
| 006 Special Cross-Validation Schemes.en.srt | 8.6 KB | ||
| 006 Special Cross-Validation Schemes.mp4 | 40.9 MB | ||
| 006 TPE - why tree-structured_.en.srt | 4.8 KB | ||
| 006 TPE - why tree-structured_.mp4 | 25.8 MB | ||
| 006 Using Scikit-learn Metrics.en.srt | 2.5 KB | ||
| 006 Using Scikit-learn Metrics.mp4 | 17.8 MB | ||
| 007 Bayesian search with Gaussian processes.en.srt | 6.8 KB | ||
| 007 Bayesian search with Gaussian processes.mp4 | 35.2 MB | ||
| 007 Datasets.html | 1.4 KB | ||
| 007 Gaussian Distribution.en.srt | 8.7 KB | ||
| 007 Gaussian Distribution.mp4 | 34.6 MB | ||
| 007 Group Cross-Validation - Demo.en.srt | 6.3 KB | ||
| 007 Random Search - Scikit-learn.mp4 | 44.2 MB | ||
| 7 | 898 KB | ||
| 007 Group Cross-Validation - Demo.mp4 | 43.3 MB | ||
| 007 Random Search - Scikit-learn.en.srt | 6.9 KB | ||
| 007 TPE with Hyperopt.en.srt | 7.8 KB | ||
| 007 TPE with Hyperopt.mp4 | 50 MB | ||
| 008 Bayes search with Random Forests.en.srt | 3.7 KB | ||
| 008 Bayes search with Random Forests.mp4 | 23 MB | ||
| 008 Multivariate Gaussian Distribution.en.srt | 19.2 KB | ||
| 008 Multivariate Gaussian Distribution.mp4 | 83.9 MB | ||
| 008 Nested Cross-Validation.en.srt | 9 KB | ||
| 008 Nested Cross-Validation.mp4 | 49.9 MB | ||
| 008 Random Search with Scikit-Optimize.en.srt | 9.8 KB | ||
| 008 Random Search with Scikit-Optimize.mp4 | 48.3 MB | ||
| 008 Set up your computer - required packages.html | 1.6 KB | ||
| 8 | 199.1 KB | ||
| 9 | 780.6 KB | ||
| 009 Bayes search with GBMs.en.srt | 3.7 KB | ||
| 009 Bayes search with GBMs.mp4 | 23 MB | ||
| 009 FAQ.html | 3.8 KB | ||
| 009 Gaussian Process.en.srt | 16 KB | ||
| 009 Gaussian Process.mp4 | 76.2 MB | ||
| 009 Nested Cross-Validation - Demo.en.srt | 8.6 KB | ||
| 009 Nested Cross-Validation - Demo.mp4 | 55.3 MB | ||
| 009 Random Search with Hyperopt.en.srt | 13.1 KB | ||
| 009 Random Search with Hyperopt.mp4 | 81.1 MB | ||
| 10 | 183.9 KB | ||
| 010 Kernels.en.srt | 7.9 KB | ||
| 010 Kernels.mp4 | 30.3 MB | ||
| 010 Parallelizing a bayesian search.en.srt | 3.3 KB | ||
| 010 Parallelizing a bayesian search.mp4 | 26.1 MB | ||
| 011 Acquisition Functions.en.srt | 15.9 KB | ||
| 011 Acquisition Functions.mp4 | 82.3 MB | ||
| 011 Bayesian search with Scikit-learn wrapper.en.srt | 5.4 KB | ||
| 011 Bayesian search with Scikit-learn wrapper.mp4 | 30.9 MB | ||
| 11 | 192.5 KB | ||
| 12 | 555.2 KB | ||
| 012 Additional Reading Resources.html | 2.2 KB | ||
| 012 Changing the kernel of a Gaussian Process.en.srt | 4.5 KB | ||
| 012 Changing the kernel of a Gaussian Process.mp4 | 25 MB | ||
| 013 Optimizing xgboost.html | 1.2 KB | ||
| 013 Scikit-Optimize - 1-Dimension.en.srt | 18.8 KB | ||
| 13 | 750.5 KB | ||
| 013 Scikit-Optimize - 1-Dimension.mp4 | 97.2 MB | ||
| 014 Optimizing parameters of a CNN.en.srt | 18.4 KB | ||
| 014 Optimizing parameters of a CNN.mp4 | 111.4 MB | ||
| 014 Scikit-Optimize - Manual Search.en.srt | 7.3 KB | ||
| 014 Scikit-Optimize - Manual Search.mp4 | 35.9 MB | ||
| 14 | 282.7 KB | ||
| 015 Analyzing the CNN search.en.srt | 8 KB | ||
| 015 Analyzing the CNN search.mp4 | 37.1 MB | ||
| 015 Scikit-Optimize - Automatic Search.en.srt | 5.4 KB | ||
| 015 Scikit-Optimize - Automatic Search.mp4 | 30.9 MB | ||
| 15 | 647.8 KB | ||
| 16 | 296.9 KB | ||
| 016 Scikit-Optimize - Alternative Kernel.en.srt | 4.5 KB | ||
| 016 Scikit-Optimize - Alternative Kernel.mp4 | 25 MB | ||
| 017 Scikit-Optimize - Neuronal Networks.en.srt | 18.4 KB | ||
| 017 Scikit-Optimize - Neuronal Networks.mp4 | 111.3 MB | ||
| 018 Scikit-Optimize - CNN - Search Analysis.en.srt | 8 KB | ||
| 018 Scikit-Optimize - CNN - Search Analysis.mp4 | 37.1 MB | ||
| TutsNode.com.txt | 102.4 B | ||
| [TGx]Downloaded from torrentgalaxy.to .txt | 614.4 B | ||
| 17 | 186.8 KB | ||
| 18 | 675.9 KB | ||
| 19 | 182.6 KB | ||
| 20 | 24.2 KB | ||
| 21 | 83 KB | ||
| 22 | 691.7 KB | ||
| 23 | 845.4 KB | ||
| 24 | 141.6 KB | ||
| 25 | 829.6 KB | ||
| 26 | 596.3 KB | ||
| 27 | 694 KB | ||
| 28 | 938.4 KB | ||
| 29 | 105.7 KB | ||
| 30 | 708.6 KB | ||
| 31 | 993.9 KB | ||
| 32 | 83.3 KB | ||
| 33 | 837.8 KB | ||
| 34 | 940.3 KB | ||
| 35 | 941.1 KB | ||
| 36 | 79.8 KB | ||
| 37 | 849 KB | ||
| 38 | 92.4 KB | ||
| 39 | 418.6 KB | ||
| 40 | 409.5 KB | ||
| 41 | 65.6 KB | ||
| 42 | 82.1 KB | ||
| 43 | 423.3 KB | ||
| 44 | 727.9 KB | ||
| 45 | 928.1 KB | ||
| 46 | 187.7 KB | ||
| 47 | 527.2 KB | ||
| 48 | 985.3 KB | ||
| 49 | 992.7 KB | ||
| 50 | 202.1 KB | ||
| 51 | 903.5 KB | ||
| 52 | 782.8 KB | ||
| 54 | 13.6 KB | ||
| 55 | 601.8 KB | ||
| 56 | 739.9 KB | ||
| 57 | 609.8 KB | ||
| 58 | 197.4 KB | ||
| 59 | 853.4 KB | ||
| 60 | 368.5 KB | ||
| 61 | 721 KB | ||
| 62 | 498.5 KB | ||
| 63 | 525.6 KB | ||
| 64 | 438.1 KB | ||
| 65 | 926 KB | ||
| ▲ 210 total files | |||
Description
Welcome to Hyperparameter Optimization for Machine Learning. In this course, you will learn multiple techniques to select the best hyperparameters and improve the performance of your machine learning models.
If you are regularly training machine learning models as a hobby or for your organization and want to improve the performance of your models, if you are keen to jump up in the leader board of a data science competition, or you simply want to learn more about how to tune hyperparameters of machine learning models, this course will show you how.
We’ll take you step-by-step through engaging video tutorials and teach you everything you need to know about hyperparameter tuning. Throughout this comprehensive course, we cover almost every available approach to optimize hyperparameters, discussing their rationale, their advantages and shortcomings, the considerations to have when using the technique and their implementation in Python.
Specifically, you will learn:
What hyperparameters are and why tuning matters
The use of cross-validation and nested cross-validation for optimization
Grid search and Random search for hyperparameters
Bayesian Optimization
Tree-structured Parzen estimators
SMAC, Population Based Optimization and other SMBO algorithms
How to implement these techniques with available open source packages including Hyperopt, Optuna, Scikit-optimize, Keras Turner and others.
By the end of the course, you will be able to decide which approach you would like to follow and carry it out with available open-source libraries.
This comprehensive machine learning course includes over 50 lectures spanning about 8 hours of video, and ALL topics include hands-on Python code examples which you can use for reference and for practice, and re-use in your own projects.
So what are you waiting for? Enroll today, learn how to tune the hyperparameters of your models and build better machine learning models.
Who this course is for:
Students who want to know more about hyperparameter optimization algorithms
Students who want to understand advanced techniques for hyperparameter optimization
Students who want to learn to use multiple open source libraries for hyperparameter tuning
Students interested in building better performing machine learning models
Students interested in participating in data science competitions
Students seeking to expand their breadth of knowledge on machine learning
Requirements
Python programming, including knowledge of NumPy, Pandas and Scikit-learn
Familiarity with basic machine learning algorithms, i.e., regression, support vector machines and nearest neighbours
Familiarity with decision tree algorithms and Random Forests
Familiarity with gradient boosting machines, i.e., xgboost, lightGBMs
Understanding of machine learning model evaluation metrics
Familiarity with Neuronal Networks
Last Updated 5/2021
All Comments