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Results 11-20 of 21 (Search time: 0.113 seconds).
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  • Sách/Book

  • Doing data science in R

    • Authors: Mark Andrews (2021)

  • Focuses on providing practical guidance for all aspects, helping readers get to grips with the tools, software, and statistical methods needed to provide the right type and level of analysis their data requires
  • Sách/Book

  • Artificial Intelligence and Robotics

    • Authors: Huimin Lu (2021)

  • This book provides insights into research in the field of artificial intelligence in combination with robotics technologies. The integration of artificial intelligence and robotic technologies is a highly topical area for researchers and developers from academia and industry around the globe, and it is likely that artificial intelligence will become the main approach for the next generation of robotics research.
  • Sách/Book

  • Artificial Intelligence and Deep Learning in Pathology

    • Authors: Stanley Cohen (2021)

  • Focuses heavily on applications in medicine, especially pathology, making unfamiliar material accessible and avoiding complex mathematics whenever possible. Covers digital pathology as a platform for primary diagnosis and augmentation via deep learning, whole slide imaging for 2D and 3D analysis, and general principles of image analysis and deep learning.
  • Sách/Book

  • Artificial Intelligence Techniques for Satellite Image Analysis

    • Authors: D. Jude Hemanth (2021)

  • The main objective of this book is to provide a common platform for diverse concepts in satellite image processing. In particular it presents the state-of-the-art in Artificial Intelligence (AI) methodologies and shares findings that can be translated into real-time applications to benefit humankind
  • Sách/Book

  • Deep Neural Network Design for Radar Applications

    • Authors: - (2021)

  • The book begins with three introductory chapters on radar systems and phenomenology, machine learning principles, and optimization for training common deep neural network (DNN) architectures. Subsequently, the book summarizes radar-specific issues relating to the different domain representations in which radar data may be presented to DNNs and synthetic data generation for training dataset augmentation.