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Results 2041-2050 of 2058 (Search time: 0.049 seconds).
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  • 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
  • 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.