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Results 2801-2810 of 3054 (Search time: 0.26 seconds).
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  • 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
  • Sách/Book

  • Innovative machine learning applications for cryptography

    • Authors: J. Anitha Ruth (2024)

  • "The objective of this book is to provide a comprehensive overview of recent theoretical and empirical work in the field of machine learning as it relates to cryptography and cryptanalysis"
  • Sách/Book

  • Handbook of IoT and big data

    • Authors: Vijender Kumar Solanki (2019)

  • "This multi-contributed handbook will focus on the latest workings of IoT (internet of Things) and Big Data. As the resources are limited, it's the endeavor of the authors to support and bring the information into one resource. The book will be divided into 4 sections that will cover IoT and technologies, the future of Big Data, algorithms, and case studies showing IoT and Big Data in various fields such as health care, manufacturing and automation"
  • Sách/Book

  • Data analytics and machine learning : navigating the big data landscape

    • Authors: Pushpa Singh (2024)

  • This book presents an in-depth analysis of successful data-driven initiatives, highlighting how organizations have leveraged data to drive decision-making processes, optimize operations, and achieve remarkable outcomes. Through case studies, readers gain valuable insights and learn practical strategies for implementing data analytics, big data, and machine learning solutions in their own organizations.
  • Sách/Book

  • Machine learning for materials discovery : numerical recipes and practical applications

    • Authors: N. M. Anoop Krishnan (2024)

  • This book covers broad areas of data-driven modeling, ranging from simple regression to advanced machine learning and optimization methods for applications in materials modeling and discovery. The book explains complex mathematical concepts in a lucid manner to ensure that readers from different materials domains are able to use these techniques successfully. A unique feature of this book is its hands-on aspecteach method presented herein is accompanied by a code that implements the method in open-source platforms such as Python.
  • Sách/Book

  • Data Analytics and Machine Learning for Integrated Corridor Management

    • Authors: Yashawi Karnati (2024)

  • This book takes readers on a journey through the intricate web of contemporary transportation systems, offering unparalleled insights into the strategies, technologies, and methodologies shaping the movement of people and goods in urban landscapes
  • Sách/Book

  • Python machine learning by example : unlock machine learning best practices with real-world use cases

    • Authors: Yuxi Liu (2024)

  • The fourth edition of Python Machine Learning By Example is a comprehensive guide for beginners and experienced machine learning practitioners who want to learn more advanced techniques, such as multimodal modeling. Written by experienced machine learning author and ex-Google machine learning engineer Yuxi (Hayden) Liu, this edition emphasizes best practices, providing invaluable insights for machine learning engineers, data scientists, and analysts.
  • Sách/Book

  • Role of Data-Intensive Distributed Computing Systems in Designing Data Solutions

    • Authors: Sarvesh Pandey (2023)

  • This book discusses the application of data systems and data-driven infrastructure in existing industrial systems in order to optimize workflow, utilize hidden potential, and make existing systems free from vulnerabilities. The book discusses application of data in the health sector, public transportation, the financial institutions, and in battling natural disasters, among others.
  • Sách/Book

  • Optimizing AI and Machine Learning Solutions : Your ultimate guide to building high-impact ML/AI solutions

    • Authors: Mirza Rahim Baig (2024)

  • This book approaches data science solution building using a principled framework and case studies with extensive hands-on guidance. It will teach the readers optimization at each step, whether it is problem formulation or hyperparameter tuning for deep learning models. This book keeps the reader pragmatic and guides them toward practical solutions by discussing the essential ML concepts, including problem formulation, data preparation, and evaluation techniques. Further, the reader will be able to learn how to apply model optimization with advanced algorithms, hyperparameter tuning, and strategies against overfitting.