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  • Sách/Book

  • Convergence of cloud with AI for big data analytics : foundations and innovation

    • Authors: Danda B. Rawat (2023)

  • This book covers the foundations and applications of cloud computing, AI, and Big Data and analyses their convergence for improved development and services. The 17 chapters of the book masterfully and comprehensively cover the intertwining concepts of artificial intelligence, cloud computing, and big data, all of which have recently emerged as the next-generation paradigms.
  • Sách/Book

  • All in on AI : how smart companies win big with artificial intelligenc

    • Authors: Thomas H. Davenport (2023)

  • If you're curious about the next phase in the implementation of artificial intelligence within companies, or if you're looking to adopt this powerful technology in a more robust way yourself, All-In on AI will give you a rare inside look at what the leading adopters are doing, while providing you with the tools to put AI at the core of everything you do.
  • Sách/Book

  • Deep Learning on Microcontrollers Learn How to Develop Embedded Al Applications Using TinyML

    • Authors: Atul Krishna Gupta (2023)

  • This book aims to increase accessibility to TinyML applications, particularly for professionals who lack the resources or expertise to develop and deploy them on microcontroller-based boards. The book starts by giving a brief introduction to Artificial Intelligence, including classical methods for solving complex problems. It also familiarizes you with the different ML model development and deployment tools, libraries, and frameworks suitable for embedded devices and microcontrollers.
  • Sách/Book

  • Advancement of deep learning and its applications in object detection and recognition

    • Authors: Roohie Naaz Mir (2022)

  • The book describes every newly proposed novel solution but skips through the fundamentals so that readers can see the field's cutting edge more rapidly. Moreover, unlike prior object detection publications, this project analyses deep learning-based object identification methods systematically and exhaustively, and also gives the most recent detection solutions and a collection of noteworthy research trends.
  • Sách/Book

  • Diagnosis of neurological disorders based on deep learning techniques

    • Authors: Jyotismita Chaki (2023)

  • This book is based on deep learning approaches used for the diagnosis of neurological disorders, including basics of deep learning algorithms using diagrams, data tables, and practical examples, for diagnosis of neurodegenerative and neurodevelopmental disorders.
  • Sách/Book

  • System Design for Epidemics Using Machine Learning and Deep Learning

    • Authors: G. R. Kanagachidambaresan (2023)

  • This book explores the benefits of deploying Machine Learning (ML) and Artificial Intelligence (AI) in the health care environment. The authors study different research directions that are working to serve challenges faced in building strong healthcare infrastructure with respect to the pandemic crisis. The authors take note of obstacles faced in the rush to develop and alter technologies during the Covid crisis.
  • Sách/Book

  • AI for scientific discovery

    • Authors: Janna Hastings (2023)

  • AI for Scientific Discovery provides an accessible introduction to the wide-ranging applications of artificial intelligence technologies in scientific research and discovery across the full breadth of scientific disciplines. Artificial intelligence technologies support discovery science in multiple different ways. They support literature management and synthesis, allowing the wealth of what has already been discovered and reported on to be integrated and easily accessed. They play a central role in data analysis and interpretation - in the context of what is called 'data science'. AI is also helping to combat the reproducibility crisis in scientific research, by underpinning the discovery process with AI-enabled standards and pipelines, support the management of large-scale data and...
  • 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.