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  • AI in the Lab: A Deep Learning Researcher's Starter Pack: Empowering Researchers at the Intersection of Molecular Biology and AI. DE

    AI in the Lab: A Deep Learning Researcher's Starter Pack by Abid, Nabeeha; Yousaf, Muhammad Zubair;

    Empowering Researchers at the Intersection of Molecular Biology and AI. DE

      • GET 5% OFF

      • The discount is only available for 'Alert of Favourite Topics' newsletter recipients.
      • Publisher's listprice EUR 60.90
      • The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.

        25 258 Ft (24 055 Ft + 5% VAT)
      • Discount 5% (cc. 1 263 Ft off)
      • Discounted price 23 995 Ft (22 852 Ft + 5% VAT)

    25 258 Ft

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    Availability

    printed on demand

    Why don't you give exact delivery time?

    Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.

    Product details:

    • Publisher LAP Lambert Academic Publishing
    • Date of Publication 1 January 2025
    • Number of Volumes Großformatiges Paperback. Klappenbroschur

    • ISBN 9786205640777
    • Binding Paperback
    • No. of pages100 pages
    • Size 220x150 mm
    • Language English
    • 625

    Categories

    Long description:

    Artificial Intelligence in the Lab: A Deep Learning Researcher's Starter Pack" is a practical and beginner-friendly guide that bridges deep learning with molecular biology research. It is designed for biology researchers, students, and early-stage AI enthusiasts who want to apply deep learning to biological sequences, structure prediction, and functional genomics. The book covers essential foundations of deep learning, experimental design, model building, evaluation, and real-world biological applications, including sequence analysis, protein structure prediction, and generative modeling. With clear explanations, hands-on code examples, and valuable research advice, this book empowers researchers to confidently integrate AI into their molecular biology projects.

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