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Results 61-65 of 65 (Search time: 0.031 seconds).
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  • Sách/Book

  • Explainable deep learning AI : methods and challenges

    • Authors: Jenny Benois-Pineau (2023)

  • The book overviews XAI and then covers a number of specific technical works and approaches for deep learning, ranging from general XAI methods to specific XAI applications, and finally, with user-oriented evaluation approaches.
  • Sách/Book

  • Deep learning and scientific computing with R torch

    • Authors: Sigrid Keydana (2023)

  • This book aims to be useful to (almost) everyone. Globally speaking, its purposes are threefold: Provide a thorough introduction to torch basics - both by carefully explaining underlying concepts and ideas, and showing enough examples for the reader to become "fluent" in torch. Again with a focus on conceptual explanation, show how to use torch in deep-learning applications, ranging from image recognition over time series prediction to audio classification.
  • Sách/Book

  • Classification applications with deep learning and machine learning technologies

    • Authors: Laith Abualigah (2023)

  • This book is very beneficial for early researchers/faculty who want to work in deep learning and machine learning for the classification domain. It helps them study, formulate, and design their research goal by aligning the latest technologies studies image and data classifications.
  • Sách/Book

  • Deep learning in cancer diagnostics : a feature-based transfer learning evaluation

    • Authors: Mohd Hafiz Arzmi (2023)

  • In this brief, the diagnosis of four types of common cancers, i.e., breast, lung, oral and skin, are evaluated with different state-of-the-art feature-based transfer learning models. It is expected that the findings in this book are insightful to various stakeholders in the diagnosis of cancer
  • Sách/Book

  • Time Series Algorithms Recipes Implement Machine Learning and Deep Learning Techniques with Python

    • Authors: Akshay R. Kulkarni (2023)

  • This book teaches the practical implementation of various concepts for time series analysis and modeling with Python through problem-solution-style recipes, starting with data reading and preprocessing. It begins with the fundamentals of time series forecasting using statistical modeling methods like AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average), and ARIMA (autoregressive integrated moving-average).