Description
Hyperparameter Optimization for Machine Learning is a hyperparameter optimization course published by Udemy Academy. This course covers a number of topics, the most important of which are network search, random search, Bayesian optimization, multi-fidelity models, the Optuna framework, the Hyperopt library and Scikit-Optimize. East. And… underlined. In this training, he becomes familiar with the selection techniques to select the best and most optimal meta-parameter and is able to provide the performance of car models as much as possible. There are metaparameter optimization features that in this training course learn the pros and cons as well as the functional considerations of each.
What You’ll Learn in Hyperparameter Optimization for Machine Learning
- Adjust and optimize the metaparameter and understand its importance and why
- Cross-validation method
- Adjusting and optimizing meta-parameters with network search and random and random search methods
- Bayesian optimization
- Optimization approach for tree-based Parzen estimators
- Population-based training
- Libraries and frameworks Hyperopt, Optuna, Scikit-optimize and Keras Turner
Course Specifications
Editor: Udemy
Instructors: Soledad Galli
French language
Intermediate level
Number of lessons: 94
Duration: 9 hours and 26 minutes
Course themes for 2022/3
Optimizing Hyperparameters for Machine Learning Prerequisites
Python programming, including knowledge of NumPy, Pandas and Scikit-learn
Familiarity with basic machine learning algorithms, i.e. regression, support vector machines and nearest neighbors
Familiarity with decision tree algorithms and random forests
Familiarity with gradient boosting machines i.e. xgboost, lightGBM
Understanding machine learning model evaluation metrics
Familiarity with neural networks
Pictures
Introductory video on hyperparameter optimization for machine learning
installation guide
After the clip, watch with your favorite reader.
english subtitles
Quality: 720p
Download links
File password(s): free download software
size
3.29 GB