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  • 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...
  • Sách/Book

  • Computational Methods and Deep Learning for Ophthalmology

    • Authors: D. Jude Hemanth (2023)

  • This handbook provides biomedical engineers, computer scientists, and multidisciplinary researchers with a significant resource for addressing the increase in the prevalence of diseases such as Diabetic Retinopathy, Glaucoma, and Macular Degeneration.
  • Sách/Book

  • Deep Learning with JAX

    • Authors: Grigory Sapunov (2023)

  • Deep Learning with JAX teaches you how to use JAX and its ecosystem to build neural networks. You’ll learn by exploring interesting examples including an image classification tool, an image filter application, and a massive scale neural network with distributed training across a cluster of TPUs. Discover how to work with JAX for hardware and other low-level aspects and how to solve common machine learning problems with JAX. By the time you’re finished with this awesome book, you’ll be ready to start applying JAX to your own research and prototyping!
  • Sách/Book

  • Deep learning in time series analysis

    • Authors: Arash Gharehbagh (2023)

  • The concept of deep machine learning becomes easier to understandable by paying attention to the cyclic stochastic time series and a time series whose content is non-stationary not only within the cycles, but also over the cycles as the beat to beat variations. This book introduces original deep learning methods for classification of such the time series using proposed clustering methods as the learning tools at the deep level
  • 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

  • Generative Deep Learning

    • Authors: David Foster (2023)

  • The book starts with the basics of deep learning and progresses to cutting-edge architectures. Through tips and tricks, you'll understand how to make your models learn more efficiently and become more creative.
  • 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 research applications for natural language processing

    • Authors: L. Ashok Kumar (2023)

  • This book delves into issues of natural language processing, a subset of artificial intelligence that enables computers to understand the meaning of human language using techniques of machine learning and deep learning algorithms to discern a words' semantic meanings