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Results 1151-1160 of 1488 (Search time: 0.079 seconds).
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  • Sách/Book

  • Statistical prediction and machine learning

    • Authors: John Tuhao Chen (2024)

  • "Written by an experienced statistics educator and two data scientists, this book unifies conventional statistical thinking and contemporary machine learning framework into a single overarching umbrella over data science. The book is designed to bridge the knowledge gap between conventional statistics and machine learning. It provides an accessible approach for readers with a basic statistics background to develop a mastery of machine learning. The book starts with elucidating examples in Chapter 1 and fundamentals on refined optimization in Chapter 2, which are followed by common supervised learning methods such as regressions, classification, support vector machines, tree algorithms, and range regressions. After a discussion on unsupervised learning methods, it includes a chapter ...
  • Sách/Book

  • Principles of data science : a beginner's guide to essential math and coding skills for data fluency and machine learning

    • Authors: Sinan Ozdemir (2024)

  • Throughout the book, you'll discover statistical models with which you can control and navigate even the densest or the sparsest of datasets and learn how to create powerful visualizations that communicate the stories hidden in your data. With a focus on application, this edition covers advanced transfer learning and pre-trained models for NLP and vision tasks. You'll get to grips with advanced techniques for mitigating algorithmic bias in data as well as models and addressing model and data drift.
  • Sách/Book

  • Graph-Powered Analytics and Machine Learning with TigerGraph

    • Authors: Victor Lee (2022)

  • With the rapid rise of graph databases, organizations are now implementing advanced analytics and machine learning solutions to help drive business outcomes. This practical guide shows data scientists, data engineers, architects, and business analysts how to get started with a graph database using TigerGraph, one of the leading graph database models available. You'll explore a three-stage approach to deriving value from connected data: connect, analyze, and learn.
  • Sách/Book

  • Mastering Design Patterns in Java: Essential Skills/ Ed Norex

    • Authors: - (2024)

  • Trang bị cho người đọc kiến thức sâu rộng về các mẫu thiết kế, đươc thiết kế riêng cho môi trường lập trình Java, cho phép họ giải quyết thách thức phần mềm phức tạp một cách tự tin và hiệu quả.
  • Sách/Book

  • How machine learning is innovating today's world : a concise technical guide

    • Authors: Arindam Dey (2024)

  • Provides a comprehensive understanding of the latest advancements and practical applications of machine learning techniques. Machine learning (ML), a branch of artificial intelligence, has gained tremendous momentum in recent years, revolutionizing the way we analyze data, make predictions, and solve complex problems. As researchers and practitioners in the field, the editors of this book recognize the importance of disseminating knowledge and fostering collaboration to further advance this dynamic discipline. How Machine Learning is Innovating Today's World is a timely book and presents a diverse collection of 25 chapters that delve into the remarkable ways that ML is transforming various fields and industries.
  • Sách/Book

  • Java Microservices and Containers in the Cloud: With Spring Boot, Kafka, PostgreSQL, Kubernetes, Helm, Terraform and AWS EKS

    • Authors: Binildas A. Christudas (2024)

  • Java Microservices and Containers in the Cloud offers a comprehensive guide to both architecture and programming aspects to Java microservices development, providing a fully hands-on experience. We not only describe various architecture patterns but also provide practical implementations of each pattern through code examples. Despite the focus on architecture, this book is designed to be accessible to novice developers with only basic programming skills, such as writing a "Hello World" program and using Maven to compile and run Java code. It ensures that even such readers can easily comprehend, deploy, and execute the code samples provided in the book. Regardless of your current knowledge or lack thereof in Docker, Kubernetes, and Cloud technologies, this book will empower you to de...
  • Sách/Book

  • Machine learning and deep learning techniques for medical image recognition

    • Authors: Ben Othman Soufiene (2024)

  • "Machine Learning and Deep Learning Techniques for Medical Image Recognition comprehensively reviews deep learning-based algorithms in medical image analysis problems including medical image processing. It includes a detailed review of deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks with the theory, and varied selection of techniques for semantic segmentation using deep learning principles in medical imaging supported by practical examples. The book offers important key aspects in the development and implementation of ML and DL approaches toward developing prediction tools and models and improving medical diag...
  • Sách/Book

  • Data science and machine learning for non-programmers : using SAS Enterprise miner

    • Authors: Dothang Truong (2024)

  • "As data continues to grow exponentially, knowledge of data science and machine learning has become more crucial than ever. Machine learning has grown exponentially, however, the abundance of resources can be overwhelming, making it challenging for new learners. This book aims to address this disparity and cater to learners from various non-technical fields, enabling them to utilise machine learning effectively. Adopting a hands-on approach, readers are guided through practical implementations using real datasets and SAS Enterprise Miner, a user-friendly data mining software that requires no programming. Throughout the chapters, two large datasets are used consistently, allowing readers to practice all stages of the data mining process within a cohesive project framework. This book ...
  • Sách/Book

  • Machine learning in medical imaging and computer vision

    • Authors: Amita Nandal (2023)

  • This edited book explores new and emerging technologies in the field of medical image processing using deep learning models, neural networks and machine learning architectures. Multimodal medical imaging and optimisation techniques are discussed in relation to the advances, challenges and benefits of computer-aided diagnoses