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Results 2031-2040 of 2058 (Search time: 0.075 seconds).
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  • 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

  • Mathematics of deep learning : an introduction

    • Authors: Leonid Berlyand; Pierre-Emmanuel Jabin. (2023)

  • The goal of this book is to provide a mathematical perspective on some key elements of the so-called deep neural networks (DNNs). Much of the interest in deep learning has focused on the implementation of DNN-based algorithms. Our hope is that this compact textbook will offer a complementary point of view that emphasizes the underlying mathematical ideas.
  • Sách/Book

  • Introduction to Mathematical Statistics and Its Applications

    • Authors: Richard J. Larsen (2013)

  • The authors demonstrate how and when to use statistical methods, while reinforcing the calculus that students have mastered in previous courses. Throughout theFifth Edition, the authors have added and updated examples and case studies, while also refining existing features that show a clear path from theory to practice.
  • Sách/Book

  • Machine learning and deep learning in computational toxicology

    • Authors: Huixiao Hong (2023)

  • This book is expected to provide a reference for practical applications of machine learning and deep learning in toxicological research. It is a useful guide for toxicologists, chemists, drug discovery and development researchers, regulatory scientists, government reviewers, and graduate students.
  • Sách/Book

  • Deep learning and its applications for vehicle networks

    • Authors: Fei Hu; Iftikhar Rasheed (2023)

  • Deep Learning (DL) will be an effective approach for AI-based vehicular networks and can deliver a powerful set of tools for such vehicular network dynamics. In various domains of vehicular networks, DL can be used for learning-based channel estimation, traffic flow prediction, vehicle trajectory prediction, location-prediction-based scheduling and routing, intelligent network congestion control mechanism, smart load balancing and vertical handoff control, intelligent network security strategies, virtual smart & efficient resource allocation and intelligent distributed resource allocation methods. This book is based on the work from world-famous experts on the application of DL for vehicle networks. It consists of the following five parts: (1) DL for vehicle safety and security: In ...
  • 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

  • Shallow and deep learning principles : scientific, philosophical, and logical perspectives

    • Authors: Zekai Sen (2023)

  • This book discusses Artificial Neural Networks (ANN) and their ability to predict outcomes using deep and shallow learning principles. The author first describes ANN implementation, consisting of at least three layers that must be established together with cells, one of which is input, the other is output, and the third is a hidden (intermediate) layer.
  • 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

  • Generative adversarial networks and deep learning : theory and applications

    • Authors: Roshani Raut (2023)

  • This book explores how to use generative adversarial networks in a variety of applications and emphasises their substantial advancements over traditional generative models. This book's major goal is to concentrate on cutting-edge research in deep learning and generative adversarial networks, which includes creating new tools and methods for processing text, images, and audio. A generative adversarial network (GAN) is a class of machine learning framework and is the next emerging network in deep learning applications. Generative Adversarial Networks(GANs) have the feasibility to build improved models, as they can generate the sample data as per application requirements. There are various applications of GAN in science and technology, including computer vision, security, multimedia an...