9781786078360 Something Deeply Hidden 33.1000 NZD InStock /shop/books/non-fiction /shop/books /shop/books/non-fiction/mathematics-science/science /shop/books/non-fiction/mathematics-science From the Royal Society Winton Prize winnerĪ magisterial tour, Something Deeply Hidden encompasses the cosmological and everyday implications of quantum reality and multiple universes.

In doing so, he fills a gap in the science that has existed for almost a century.Ī magisterial tour, Something Deeply Hidden encompasses the cosmological and everyday implications of quantum reality and multiple universes. Acclaimed physicist and writer Sean Carroll debunks the myths, resurrects and reinstates the Many-Worlds interpretation, and presents a new path towards solving the apparent conflict between quantum mechanics and Einstein's theory of general relativity. Spanning the history of quantum discoveries, from Einstein and Bohr to the present day, Something Deeply Hidden is the essential guide to the most intriguing subject in science. It stands as the best explanation of the fundamental nature of our world. The implications are mind-bending, and not yet fully understood, but this revolutionary theory is truly illuminating. From the Royal Society Winton Prize winner The implications are mind-bending, and not yet fully understood, but this revolutionary theory is truly. Sean Carroll’s new (available in stores early September) book, Something Deeply Hidden, is a quite good introduction to issues in the understanding of quantum mechanics, unfortunately wrapped in a book cover and promotional campaign of utter nonsense. It's fascinating to see a real working physicist thinking these things through and trying to come to a conclusion.' - Professor Brian Cox on The Big Picture, a Mail on Sunday Book of the Year 'Sean Carroll examines what it means to exist on this speck of dust in a possibly infinite universe. Results on MNIST, CIFAR-10, CIFAR-100, and SVHN).From the Royal Society Winton Prize winner Significant performance gain over existing methods (e.g. Method is evident and our experimental result on benchmark datasets shows Stochastic gradient methods to analyze our algorithm. Hidden layers, in addition to the overall objective at the output layer (aĭifferent strategy to layer-wise pre-training). We introduce 'companion objective' to the individual Layers (3) effectiveness in training due to the presence of the exploding and Of the intermediate layers to the overall classification (2)ĭiscriminativeness and robustness of learned features, especially in the early Neural networks (CNN) style architectures are being looked at: (1) transparency Studying a new formulation in deep networks. We make an attempt to boost the classification performance by Download a PDF of the paper titled Deeply-Supervised Nets, by Chen-Yu Lee and 4 other authors Download PDF Abstract: Our proposed deeply-supervised nets (DSN) method simultaneously minimizesĬlassification error while making the learning process of hidden layers directĪnd transparent.

Deeply hidden