Skip to product information
1 of 1

Stock cover image; actual copy may differ.

Bayesian Optimization: Theory and Practice Using Python

Regular price
$15.22 USD
Regular price
Sale price
$15.22 USD

Book details

Liu, Peng

Condition
Good
Format
Paperback
ISBN
9781484290620

Has a sturdy binding with some shelf wear. May have some markings or highlighting. Used copies may not include access codes or Cd's. Slight bending may be present.

Shipping calculated at checkout.
Description
This book covers the essential theory and implementation of popular Bayesian optimization techniques in an intuitive and well-illustrated manner. The techniques covered in this book will enable you to better tune the hyperparemeters of your machine learning models and learn sample-efficient approaches to global optimization. The book begins by introducing different Bayesian Optimization (BO) techniques, covering both commonly used tools and advanced topics. It follows a “develop from scratch” method using Python, and gradually builds up to more advanced libraries such as BoTorch, an open-source project introduced by Facebook recently. Along the way, you’ll see practical implementations of this important discipline along with thorough coverage and straightforward explanations of essential theories. This book intends to bridge the gap between researchers and practitioners, providing both with a comprehensive, easy-to-digest, and useful reference guide. After completingthis book, you will have a firm grasp of Bayesian optimization techniques, which you’ll be able to put into practice in your own machine learning models. What You Will Learn Apply Bayesian Optimization to build better machine learning models Understand and research existing and new Bayesian Optimization techniques Leverage high-performance libraries such as BoTorch, which offer you the ability to dig into and edit the inner working Dig into the inner workings of common optimization algorithms used to guide the search process in Bayesian optimization Who This Book Is ForBeginner to intermediate level professionals in machine learning, analytics or other roles relevant in data science.
ASIN: 1484290623
VSKU: GBV.1484290623.G
Condition: Good
Author/Artist:Liu, Peng
Binding: Paperback
Note: Any images shown are stock photographs and product may differ from what is shown.
Condition Notes: Has a sturdy binding with some shelf wear. May have some markings or highlighting. Used copies may not include access codes or Cd's. Slight bending may be present.
Shipping

We currently ship retail orders within the United States. Orders are normally prepared for carrier handoff within 1–2 business days, excluding weekends and holidays. Carrier transit time is additional.

Available services and charges depend on order value, package weight, destination, and the method selected. Checkout shows the applicable options before payment; not every order qualifies for free shipping.

Read the shipping policy.

Returns

You may request a return within 30 calendar days after delivery. Contact us before sending a book back.

Customers pay change-of-mind return postage. For a wrong, damaged, or materially misdescribed item, we provide a prepaid return label or another resolution you accept. See the policy for eligibility, original shipping charges, and refund timing.

Read the returns and refund policy.

Bayesian Optimization: Theory and Practice Using Python — used book cover