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  • 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

  • Modern deep learning for tabular data : novel approaches to common modeling problems

    • Authors: Zian Wang; Andre Ye (2023)

  • Who This Book Is For Data scientists and researchers of all levels from beginner to advanced looking to level up results on tabular data with deep learning or to understand the theoretical and practical aspects of deep tabular modeling research. Applicable to readers seeking to apply deep learning to all sorts of complex tabular data contexts, including business, finance, medicine, education, and security
  • Sách/Book

  • Deep learning for healthcare decision making

    • Authors: Vishal Jain (2023)

  • "This book is an attempt to unveil the hidden potential of the enormous amount of health information and technology. This book is written with the intent to uncover the stakes and possibilities involved in realizing personalized health-care services through efficient and effective deep learning algorithms"--
  • 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).