Hyperparameter tuning with Python : boost your machine learning model's performance via hyperparameter tuning / Louis Owen.

Format:

Book

Author/Creator:

Owen, Louis, author.

Language:

English

Subjects (All):

Machine learning.

Python (Computer program language).

Physical Description:

1 online resource (306 pages)

Place of Publication:

Birmingham, England : Packt Publishing, [2022]

Biography/History:

Owen Louis: Louis Owen is a data scientist/AI engineer from Indonesia who is always hungry for new knowledge. Throughout his career journey, he has worked in various fields of industry, including NGOs, e-commerce, conversational AI, OTA, Smart City, and FinTech. Outside of work, he loves to spend his time helping data science enthusiasts to become data scientists, either through his articles or through mentoring sessions. He also loves to spend his spare time doing his hobbies: watching movies and conducting side projects. Finally, Louis loves to meet new friends! So, please feel free to reach out to him on LinkedIn if you have any topics to be discussed.

Summary:

This book curates numerous hyperparameter tuning methods for Python all in one place, providing a deep explanation of how each method works, and a decision map that can help you choose which hyperparameter tuning method is right for your specific problem and situation.

Contents:

Table of Contents Evaluating Machine Learning Models Introducing Hyperparameter Tuning Exploring Exhaustive Search Exploring Bayesian Optimization Exploring Heuristic Search Exploring Multi-Fidelity Optimization Hyperparameter Tuning via Scikit Hyperparameter Tuning via Hyperopt Hyperparameter Tuning via Optuna Advanced Hyperparameter Tuning with DEAP and Microsoft NNI Understanding Hyperparameters of Popular Algorithms Introducing Hyperparameter Tuning Decision Map Tracking Hyperparameter Tuning Experiments Conclusions and Next Steps.

Notes:

Includes index.

Description based on print version record.

ISBN:

9781803241944

1803241942

OCLC:

1338299408