Description: Hyperparameter Optimization in Machine Learning Please note: this item is printed on demand and will take extra time before it can be dispatched to you (up to 20 working days). Make Your Machine Learning and Deep Learning Models More Efficient Author(s): Tanay Agrawal Format: Paperback Publisher: APress, United States Imprint: APress ISBN-13: 9781484265789, 978-1484265789 Synopsis Dive into hyperparameter tuning of machine learning models and focus on what hyperparameters are and how they work. This book discusses different techniques of hyperparameters tuning, from the basics to advanced methods. This is a step-by-step guide to hyperparameter optimization, starting with what hyperparameters are and how they affect different aspects of machine learning models. It then goes through some basic (brute force) algorithms of hyperparameter optimization. Further, the author addresses the problem of time and memory constraints, using distributed optimization methods. Next you'll discuss Bayesian optimization for hyperparameter search, which learns from its previous history. The book discusses different frameworks, such as Hyperopt and Optuna, which implements sequential model-based global optimization (SMBO) algorithms. During these discussions, you'll focus on different aspects such as creation of search spaces and distributed optimization of these libraries. Hyperparameter Optimization in Machine Learning creates an understanding of how these algorithms work and how you can use them in real-life data science problems. The final chapter summaries the role of hyperparameter optimization in automated machine learning and ends with a tutorial to create your own AutoML script. Hyperparameter optimization is tedious task, so sit back and let these algorithms do your work. What You Will Learn Discover how changes in hyperparameters affect the model's performance. Apply different hyperparameter tuning algorithms to data science problems Work with Bayesian optimization methods to create efficient machine learning and deep learning models Distribute hyperparameter optimization using a cluster of machines Approach automated machine learning using hyperparameter optimization Who This Book Is For Professionals and students working with machine learning.
Price: 36.05 GBP
Location: Aldershot
End Time: 2024-11-27T12:04:09.000Z
Shipping Cost: 25.89 GBP
Product Images
Item Specifics
Return postage will be paid by: Buyer
Returns Accepted: Returns Accepted
After receiving the item, your buyer should cancel the purchase within: 60 days
Return policy details:
Book Title: Hyperparameter Optimization in Machine Learning
Number of Pages: 166 Pages
Language: English
Publication Name: Hyperparameter Optimization in Machine Learning: Make Your Machine Learning and Deep Learning Models More Efficient
Publisher: Apress
Publication Year: 2020
Subject: Computer Science
Item Height: 235 mm
Item Weight: 296 g
Type: Textbook
Author: Tanay Agrawal
Item Width: 155 mm
Format: Paperback
