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Tag: Sequence

  • Supervised Sequence Labelling with Recurrent Neural Networks (Studies in Computational Intelligence, 385)

    Supervised Sequence Labelling with Recurrent Neural Networks (Studies in Computational Intelligence, 385)


    Price: $199.99 – $154.98
    (as of Dec 24,2024 05:37:34 UTC – Details)




    Publisher ‏ : ‎ Springer; 2012th edition (February 9, 2012)
    Language ‏ : ‎ English
    Hardcover ‏ : ‎ 160 pages
    ISBN-10 ‏ : ‎ 3642247962
    ISBN-13 ‏ : ‎ 978-3642247965
    Item Weight ‏ : ‎ 12 ounces
    Dimensions ‏ : ‎ 6.5 x 0.75 x 9.5 inches


    Supervised Sequence Labelling with Recurrent Neural Networks: A Deep Dive into the Latest Research (Studies in Computational Intelligence, 385)

    In recent years, recurrent neural networks (RNNs) have emerged as a powerful tool for sequence labelling tasks such as named entity recognition, part-of-speech tagging, and speech recognition. These models have the ability to capture long-range dependencies in sequential data, making them well-suited for tasks where context is crucial.

    In the book “Supervised Sequence Labelling with Recurrent Neural Networks” (Studies in Computational Intelligence, 385), leading researchers in the field provide a comprehensive overview of the latest advancements in this area. The book covers topics such as the theoretical foundations of RNNs, practical considerations for training and optimizing these models, and state-of-the-art applications in natural language processing, bioinformatics, and more.

    Whether you are a seasoned researcher looking to stay up-to-date on the latest developments in sequence labelling, or a practitioner interested in applying RNNs to real-world problems, this book is a valuable resource. With contributions from experts in academia and industry, “Supervised Sequence Labelling with Recurrent Neural Networks” offers a thorough exploration of the capabilities and limitations of RNNs for sequence labelling tasks.

    Pick up your copy today and dive into the exciting world of supervised sequence labelling with recurrent neural networks.
    #Supervised #Sequence #Labelling #Recurrent #Neural #Networks #Studies #Computational #Intelligence

  • Machine-Learning Based Sequence Analysis, Bioinformatics and Nanopore

    Machine-Learning Based Sequence Analysis, Bioinformatics and Nanopore



    Machine-Learning Based Sequence Analysis, Bioinformatics and Nanopore

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    Machine-learning based sequence analysis, bioinformatics, and nanopore technology are revolutionizing the way we study and understand biological systems. By combining the power of machine learning algorithms with the high-resolution data generated by nanopore sequencing technology, researchers are able to unravel complex biological processes with unprecedented accuracy and speed.

    One of the key advantages of using machine learning in sequence analysis and bioinformatics is its ability to quickly identify patterns and correlations within large datasets. This can help researchers uncover hidden relationships between genes, proteins, and other biological molecules, leading to new insights into the underlying mechanisms of disease and other biological phenomena.

    Nanopore sequencing technology, on the other hand, offers researchers the ability to directly sequence DNA and RNA molecules in real-time, providing a more detailed and dynamic view of the genome. This technology is particularly useful for studying complex genomic regions, such as repetitive sequences or structural variations, which are difficult to analyze using traditional sequencing methods.

    By combining machine learning algorithms with nanopore sequencing technology, researchers can analyze vast amounts of genomic data with unprecedented speed and accuracy. This has the potential to revolutionize fields such as personalized medicine, drug discovery, and agriculture, by enabling researchers to quickly and accurately identify biomarkers, drug targets, and other important biological information.

    Overall, the combination of machine learning, bioinformatics, and nanopore technology holds great promise for advancing our understanding of biology and accelerating the pace of scientific discovery. With continued advancements in these fields, we can expect to see even more exciting breakthroughs in the future.
    #MachineLearning #Based #Sequence #Analysis #Bioinformatics #Nanopore