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1. |
Record Nr. |
UNISA996392110503316 |
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Titolo |
An ease for overseers of the poore [[electronic resource] ] : abstracted from the statutes, allowed by practise, and now reduced into forme, as a necessarie directorie for imploying, releeuing, and ordering of the poore. With an easie and readie table for recording the number, names, ages, exercises and defects of the poore, fit to be obserued of the ouerseers in euery parish. Also hereunto is annexed a prospect for rich men to induce them to giue, and a patterne for poore men to prouoke them to labour, very pertinent to the matter. The principall heads hereof appeare in the next page |
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Pubbl/distr/stampa |
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[London], : Printed by Iohn Legat, printer to the Vniuersitie of Cambridge, 1601 |
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Descrizione fisica |
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Soggetti |
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Poor laws - Great Britain |
Public welfare - Great Britain |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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See also STC 9494.9--STC. |
Reproduction of the original in the British Library. |
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Sommario/riassunto |
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2. |
Record Nr. |
UNINA9910688417703321 |
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Autore |
Javier Borge-Holthoefer |
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Titolo |
At the Crossroads: Lessons and Challenges in Computational Social Science |
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Pubbl/distr/stampa |
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Descrizione fisica |
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1 online resource (98 p.) |
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Collana |
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Frontiers Research Topics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Sommario/riassunto |
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The interest of physicists in economic and social questions is not new: for over four decades, we have witnessed the emergence of what is called nowadays "sociophysics" and "econophysics", vigorous and challenging areas within the wider "Interdisciplinary Physics". With tools borrowed from Statistical Physics and Complexity, this new area of study have already made important contributions, which in turn have fostered the development of novel theoretical foundations in Social Science and Economics, via mathematical approaches, agent-based modelling and numerical simulations. From these foundations, Computational Social Science has grown to incorporate as well the empirical component -aided by the recent data deluge from the Web 2.0 and 3.0-, closing in this way the experiment-theory cycle in the best tradition of Physics. |
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3. |
Record Nr. |
UNINA9910300747503321 |
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Autore |
Masters Timothy |
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Titolo |
Deep Belief Nets in C++ and CUDA C: Volume 1 : Restricted Boltzmann Machines and Supervised Feedforward Networks / / by Timothy Masters |
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Pubbl/distr/stampa |
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Berkeley, CA : , : Apress : , : Imprint : Apress, , 2018 |
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ISBN |
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Edizione |
[1st ed. 2018.] |
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Descrizione fisica |
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1 online resource (225 pages) : illustrations |
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Disciplina |
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Soggetti |
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Artificial intelligence |
Programming languages (Electronic computers) |
Big data |
Artificial Intelligence |
Programming Languages, Compilers, Interpreters |
Big Data |
Big Data/Analytics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di contenuto |
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1. Introduction -- 2. Supervised Feedforward Networks -- 3. Restricted Boltzmann Machines -- 4. Greedy Training: Generative Samplings -- 5. DEEP Operating Manual. |
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Sommario/riassunto |
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Discover the essential building blocks of the most common forms of deep belief networks. At each step this book provides intuitive motivation, a summary of the most important equations relevant to the topic, and concludes with highly commented code for threaded computation on modern CPUs as well as massive parallel processing on computers with CUDA-capable video display cards. The first of three in a series on C++ and CUDA C deep learning and belief nets, Deep Belief Nets in C++ and CUDA C: Volume 1 shows you how the structure of these elegant models is much closer to that of human brains than traditional neural networks; they have a thought process that is capable of learning abstract concepts built from simpler primitives. As such, you’ll see that a typical deep belief net can learn to recognize complex patterns by optimizing millions of parameters, yet this model can still |
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be resistant to overfitting. All the routines and algorithms presented in the book are available in the code download, which also contains some libraries of related routines. You will: Employ deep learning using C++ and CUDA C Work with supervised feedforward networks Implement restricted Boltzmann machines Use generative samplings Discover why these are important. |
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