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  • 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 for crack-like object detection

    • Authors: Kaige Zhang (2023)

  • With the development of artificial intelligence (AI), the deep learning technique has achieved great success. However, using deep learning for high accurate crack localization is non-trivial. Based on deep learning, this book has solved a bunch of important issues existing in crack-like object detection, and finished a practical smart pavement surface inspection system. By introducing those method and the system, this book gives the reader an easy way to get into the computer vision and deep learning research area. In addition, this research performs a preliminary study about the future AI system, which provides a concept that has potential to realize fully automatic crack detection without human's intervention
  • 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

  • Principles of Microeconomics

    • Authors: Gans, Joshua (2022)

  • Principles of Microeconomics 8th edition focuses on the 10 core principles of economics to provide you with a clear understanding of the discipline. With an approachable, student-friendly writing style this resource will help you to quickly grasp economic concepts and build a strong understand of how economics applies to the real world.
  • 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

  • Workbook for Principles of Microeconomics

    • Authors: Kolmar , Martin (2022)

  • The textbook pursues an integrative approach to modern microeconomics by critically reflecting on the main findings of economics from a philosophical standpoint and comparing them to approaches found in the social sciences. It adopts an institutional perspective to analyze the potential and limitations of different market types, and highlights implications for the design of the legal system and business practices throughout. In addition to traditional rational-choice models, important findings from behavioral economics and psychology are also presented.
  • 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).