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  • Deep Learning in Science

    Deep Learning in Science by Baldi, Pierre;

      • GET 10% OFF

      • The discount is only available for 'Alert of Favourite Topics' newsletter recipients.
      • Publisher's listprice GBP 54.00
      • 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.

        27 329 Ft (26 028 Ft + 5% VAT)
      • Discount 10% (cc. 2 733 Ft off)
      • Discounted price 24 596 Ft (23 425 Ft + 5% VAT)

    27 329 Ft

    db

    Availability

    Estimated delivery time: In stock at the publisher, but not at Prospero's office. Delivery time approx. 3-5 weeks.
    Not in stock at Prospero.

    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.

    Short description:

    Rigorous treatment of the theory of deep learning from first principles, with applications to beautiful problems in the natural sciences.

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    Long description:

    This is the first rigorous, self-contained treatment of the theory of deep learning. Starting with the foundations of the theory and building it up, this is essential reading for any scientists, instructors, and students interested in artificial intelligence and deep learning. It provides guidance on how to think about scientific questions, and leads readers through the history of the field and its fundamental connections to neuroscience. The author discusses many applications to beautiful problems in the natural sciences, in physics, chemistry, and biomedicine. Examples include the search for exotic particles and dark matter in experimental physics, the prediction of molecular properties and reaction outcomes in chemistry, and the prediction of protein structures and the diagnostic analysis of biomedical images in the natural sciences. The text is accompanied by a full set of exercises at different difficulty levels and encourages out-of-the-box thinking.

    'A splendid and timely contribution to the oeuvre in a rapidly burgeoning field. The text takes the reader on a brisk tour of deep learning from its origins in neuroscience to the current state of the art and future directions, all buttressed with a palette of rich applications in the natural sciences. A noteworthy feature of the exposition is the focus on not only the mechanisms themselves but on the explication of the guiding principles behind them. This text will support readers of various persuasions, from students who wish to absorb the basic principles informing the current approaches to deep learning, to practitioners in the natural sciences who wish to explore what deep learning has to offer in a panoply of complex problems.' Santosh S. Venkatesh, Professor of Electrical and Systems Engineering, University of Pennsylvania

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    Table of Contents:

    1. Introduction; 2. Basic Concepts; 3. Shallow Networks and Shallow Learning; 4. Two-Layer Networks and Universal Approximation; 5. Autoencoders; 6. Deep Networks and Backpropagation; 7. The Local Learning Principle; 8. The Deep Learning Channel; 9. Recurrent Networks; 10. Recursive Networks; 11. Applications in Physics; 12. Applications in Chemistry; 13. Applications in Biology and Medicine; 14. Conclusion; Appendix A. Reinforcement Learning and Deep Reinforcement Learning; Appendix B. Hints and Remarks for Selected Exercises; References; Index.

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