MOMENTAN AUSVERKAUFT
Über dieses Produkt
Product Identifiers
PublisherManning Publications Co. LLC
ISBN-101617299790
ISBN-139781617299797
eBay Product ID (ePID)8057270284
Product Key Features
Number of Pages272 Pages
LanguageEnglish
Publication NameFeature Engineering Bookcamp
Publication Year2022
SubjectData Modeling & Design, Neural Networks
TypeTextbook
AuthorSinan Ozdemir
Subject AreaComputers
FormatTrade Paperback
Dimensions
Item Height0.7 in
Item Weight12.7 Oz
Item Length9.2 in
Item Width7.4 in
Additional Product Features
LCCN2022-286717
Dewey Edition23
IllustratedYes
Dewey Decimal006.31
Table Of Contenttable of contents detailed TOC PART 1: FIRST TIME ON A BOAT: INTRODUCTION TO KUBERNETES READ IN LIVEBOOK 1INTRODUCING KUBERNETES READ IN LIVEBOOK 2UNDERSTANDING CONTAINERS READ IN LIVEBOOK 3DEPLOYING YOUR FIRST APPLICATION PART 2: LEARNING THE ROPES: KUBERNETES API OBJECTS READ IN LIVEBOOK 4INTRODUCING KUBERNETES API OBJECTS READ IN LIVEBOOK 5RUNNING WORKLOADS IN PODS READ IN LIVEBOOK <a href="https://livebook.manning.com/book/kubernetes-in-action-second-edition/chapter-6?origin=product-toc" title="Read in liveB
SynopsisKubernetes is an essential tool for anyone deploying and managing cloud-native applications. It lays out a complete introduction to container technologies and containerized applications along with practical tips for efficient deployment and operation. This revised edition of the bestselling Kubernetes in Action contains new coverage of the Kubernetes architecture, including the Kubernetes API, and a deep dive into managing a Kubernetes cluster in production. In Kubernetes in Action, Second Edition , you'll start with an overview of how Docker containers work with Kubernetes and move quickly to building your first cluster. You'll gradually expand your initial application, adding features and deepening your knowledge of Kubernetes architecture and operation. As you navigate this comprehensive guide, you'll also appreciate thorough coverage of high-value topics like monitoring, tuning, and scaling Kubernetes in Action, Second Edition teaches you to use Kubernetes to deploy container-based distributed applications. You'll start with an overview of how Docker containers work with Kubernetes and move quickly to building your first cluster. You'll gradually expand your initial application, adding features and deepening your knowledge of Kubernetes architecture and operation. In this revised and expanded second edition, you'll take a deep dive into the structure of a Kubernetes-based application and discover how to manage a Kubernetes cluster in production. As you navigate this comprehensive guide, you'll also appreciate thorough coverage of high-value topics like monitoring, tuning, and scaling., Kubernetes is an essential tool for anyone deploying and managing cloud-native applications. It lays out a complete introduction to container technologies and containerized applications along with practical tips for efficient deployment and operation. This revised edition of the bestselling Kubernetes in Action contains new coverage of the Kubernetes architecture, including the Kubernetes API, and a deep dive into managing a Kubernetes cluster in production.In Kubernetes in Action, Second Edition , you'll start with an overview of how Docker containers work with Kubernetes and move quickly to building your first cluster. You'll gradually expand your initial application, adding features and deepening your knowledge of Kubernetes architecture and operation. As you navigate this comprehensive guide, you'll also appreciate thorough coverage of high-value topics like monitoring, tuning, and scaling., Deliver huge improvements to your machine learning pipelines without spending hours fine-tuning parameters! This book's practical case-studies reveal feature engineering techniques that upgrade your data wrangling--and your ML results. In Feature Engineering Bookcamp you will learn how to: Identify and implement feature transformations for your data Build powerful machine learning pipelines with unstructured data like text and images Quantify and minimize bias in machine learning pipelines at the data level Use feature stores to build real-time feature engineering pipelines Enhance existing machine learning pipelines by manipulating the input data Use state-of-the-art deep learning models to extract hidden patterns in data Feature Engineering Bookcamp guides you through a collection of projects that give you hands-on practice with core feature engineering techniques. You'll work with feature engineering practices that speed up the time it takes to process data and deliver real improvements in your model's performance. This instantly-useful book skips the abstract mathematical theory and minutely-detailed formulas; instead you'll learn through interesting code-driven case studies, including tweet classification, COVID detection, recidivism prediction, stock price movement detection, and more. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Get better output from machine learning pipelines by improving your training data! Use feature engineering, a machine learning technique for designing relevant input variables based on your existing data, to simplify training and enhance model performance. While fine-tuning hyperparameters or tweaking models may give you a minor performance bump, feature engineering delivers dramatic improvements by transforming your data pipeline. About the book Feature Engineering Bookcamp walks you through six hands-on projects where you'll learn to upgrade your training data using feature engineering. Each chapter explores a new code-driven case study, taken from real-world industries like finance and healthcare. You'll practice cleaning and transforming data, mitigating bias, and more. The book is full of performance-enhancing tips for all major ML subdomains--from natural language processing to time-series analysis. What's inside Identify and implement feature transformations Build machine learning pipelines with unstructured data Quantify and minimize bias in ML pipelines Use feature stores to build real-time feature engineering pipelines Enhance existing pipelines by manipulating input data About the reader For experienced machine learning engineers familiar with Python. About the author Sinan Ozdemir is the founder and CTO of Shiba, a former lecturer of Data Science at Johns Hopkins University, and the author of multiple textbooks on data science and machine learning. Table of Contents 1 Introduction to feature engineering 2 The basics of feature engineering 3 Healthcare: Diagnosing COVID-19 4 Bias and fairness: Modeling recidivism 5 Natural language processing: Classifying social media sentiment 6 Computer vision: Object recognition 7 Time series analysis: Day trading with machine learning 8 Feature stores 9 Putting it all together
LC Classification NumberQ325.5
![Feature Engineering Bookcamp by Ozdemir, Sinan [Paperback]](https://i.ebayimg.com/images/g/gkQAAOSwUJFnCXa4/s-l225.jpg)
