Udemy - Hyperparameter Optimization for Machine Learning 2021
Hyperparameter Optimization for Machine Learning

Udemy – Hyperparameter Optimization for Machine Learning 2021

Description

Hyperparameter Optimization for Machine Learning is a hyperparameter optimization training course for machine learning 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, Optuna framework, Hyperopt library, and Scikit-Optimize. And…

pointed out. In this training course, you will be introduced to various techniques for selecting the best and most optimal meta-parameter, and you will be able to improve the performance of machine learning models as much as possible. There are several approaches to optimizing meta-parameters, and in this course, you will learn about the advantages and disadvantages, as well as the functional considerations of each.

What you will learn in the Hyperparameter Optimization for Machine Learning course:

  • Adjust and optimize the meta parameter and understand its importance and why
  • Cross-validation method
  • Adjustment and optimization of meta-parameters with the methods of network search method and the random and random search
  • Bayesian optimization
  • Tree-Structured Parzen Estimators Optimization Approach
  • Population-Based Training
  • HyperoptOptuna , Scikit-optimize, and Keras Turner libraries and frameworks

Course specifications

Publisher: Udemy
Instructor: Soledad Galli
Language: English
Education Level: Average
Number of Courses: 94
Training Duration: 9 hours and 26 minutes

See Also:

Udemy – SQL & PostgreSQL for Beginners: Become an SQL Expert 2020

Udemy – GraphQL by Example 2021

Udemy – Complete 2022 Data Science & Machine Learning Bootcamp 2020

Udemy – LS DYNA – A Simulation Training with Practical Applications 2020

Udemy – Windows Server 2019 Administration 2021

Course topics on 2022/3

 

Hyperparameter Optimization for Machine Learning

Prerequisites for Hyperparameter Optimization for Machine Learning

Python programming, including knowledge of NumPy, Pandas, and Scikit-learn

Familiarity with basic machine learning algorithms, ie, regression, support vector machines, and nearest neighbors

Familiarity with decision tree algorithms and Random Forests

Familiarity with gradient boosting machines, ie, xgboost, lightGBMs

Understanding of machine learning model evaluation metrics

Familiarity with Neuronal Networks

Course pictures

 

Hyperparameter Optimization for Machine Learning

Installation guide

After Extract, watch with your favorite Player.

English subtitle

Quality: 720p

download link

Download Part 1 – 1 GB
Download Part 2 – 1 GB
Download Section 3 – 1 GB
Download Section 4 – 291 MB
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