Deep Learning in Science

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

Tartalomjegyzék: