• Death's End by Cixin Liu

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    • Death's End
    • Cixin Liu
    • Page: 592
    • Format: pdf, ePub, mobi, fb2
    • ISBN: 9780765377104
    • Publisher: Tom Doherty Associates

     

    Death's End

     

     

     

    Free downloads books in pdf Death's End by Cixin Liu in English 9780765377104

     

    Overview

    With The Three-Body Problem, English-speaking readers got their first chance to experience the multiple-award-winning and bestselling Three-Body Trilogy by China's most beloved science fiction author, Cixin Liu. Three-Body was released to great acclaim including coverage in The New York Times and The Wall Street Journal. It was also named a finalist for the Nebula Award, making it the first translated novel to be nominated for a major SF award since Italo Calvino's Invisible Cities in 1976. Now this epic trilogy concludes with Death's End. Half a century after the Doomsday Battle, the uneasy balance of Dark Forest Deterrence keeps the Trisolaran invaders at bay. Earth enjoys unprecedented prosperity due to the infusion of Trisolaran knowledge. With human science advancing daily and the Trisolarans adopting Earth culture, it seems that the two civilizations will soon be able to co-exist peacefully as equals without the terrible threat of mutually assured annihilation. But the peace has also made humanity complacent. Cheng Xin, an aerospace engineer from the early 21st century, awakens from hibernation in this new age. She brings with her knowledge of a long-forgotten program dating from the beginning of the Trisolar Crisis, and her very presence may upset the delicate balance between two worlds. Will humanity reach for the stars or die in its cradle?



     

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  • Fanaroff and Martin's Neonatal-Perinatal Medicine, 2-Volume Set: Diseases of the Fetus and Infant by Richard J. Martin MBBS, FRACP, Avroy A. Fanaroff MB, FRCPE, FRCPCH, Michele C. Walsh MD, MSE

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    • Fanaroff and Martin's Neonatal-Perinatal Medicine, 2-Volume Set: Diseases of the Fetus and Infant
    • Richard J. Martin MBBS, FRACP, Avroy A. Fanaroff MB, FRCPE, FRCPCH, Michele C. Walsh MD, MSE
    • Page: 2157
    • Format: pdf, ePub, mobi, fb2
    • ISBN: 9780323567114
    • Publisher: Elsevier Health Sciences

     

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  • The Salt Path. Raynor Winn

     

    The Salt Path

     


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    ISBN: 9780143134114 | 288 pages | 8 Mb
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    • The Salt Path
    • Raynor Winn
    • Page: 288
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    • ISBN: 9780143134114
    • Publisher: Penguin Publishing Group

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  • Deep Learning by John D. Kelleher

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    • Deep Learning
    • John D. Kelleher
    • Page: 296
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    • ISBN: 9780262537551
    • Publisher: MIT Press

     

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    Overview

    An accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars. Deep learning is an artificial intelligence technology that enables computer vision, speech recognition in mobile phones, machine translation, AI games, driverless cars, and other applications. When we use consumer products from Google, Microsoft, Facebook, Apple, or Baidu, we are often interacting with a deep learning system. In this volume in the MIT Press Essential Knowledge series, computer scientist John Kelleher offers an accessible and concise but comprehensive introduction to the fundamental technology at the heart of the artificial intelligence revolution. Kelleher explains that deep learning enables data-driven decisions by identifying and extracting patterns from large datasets; its ability to learn from complex data makes deep learning ideally suited to take advantage of the rapid growth in big data and computational power. Kelleher also explains some of the basic concepts in deep learning, presents a history of advances in the field, and discusses the current state of the art. He describes the most important deep learning architectures, including autoencoders, recurrent neural networks, and long short-term networks, as well as such recent developments as Generative Adversarial Networks and capsule networks. He also provides a comprehensive (and comprehensible) introduction to the two fundamental algorithms in deep learning: gradient descent and backpropagation. Finally, Kelleher considers the future of deep learning—major trends, possible developments, and significant challenges.



     

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