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  • Prospero Book Market Podcast

  • Advanced Deep Learning for Engineers and Scientists: A Practical Approach
      • GET 20% OFF

      • The discount is only available for 'Alert of Favourite Topics' newsletter recipients.
      • Publisher's listprice EUR 90.94
      • 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.

        37 717 Ft (35 921 Ft + 5% VAT)
      • Discount 20% (cc. 7 543 Ft off)
      • Discounted price 30 174 Ft (28 737 Ft + 5% VAT)

    37 717 Ft

    db

    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.

    Long description:

    This book provides a complete illustration of deep learning concepts with case-studies and practical examples useful for real time applications. This book introduces a broad range of topics in deep learning. The authors start with the fundamentals, architectures, tools needed for effective implementation for scientists. They then present technical exposure towards deep learning using Keras, Tensorflow, Pytorch and Python. They proceed with advanced concepts with hands-on sessions for deep learning. Engineers, scientists, researches looking for a practical approach to deep learning will enjoy this book.

    • Presents practical basics to advanced concepts in deep learning and how to apply them through various projects;
    • Discusses topics such as deep learning in smart grids and renewable energy & sustainable development;
    • Explains how to implement advanced techniques in deep learning using Pytorch, Keras, Python programming.

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    Table of Contents:

    Introduction.- Introduction to ANN.- Introduction to Deep Learning.- Deep Soft Computing using Python.- Working with Keras.- Deep learning Applications using Python.- Advanced Deep learning techniques.- Conclusion.

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