Détail de l'éditeur
Springer Nature
Commentaire :
Evolution de Springer après fusion avec Nature publishing en 2016
Collections rattachées :
|
Documents disponibles chez cet éditeur (34)
Ajouter le résultat dans votre panier Affiner la recherche Interroger des sources externes
Titre : Fundamentals of Java programming Type de document : Guide/Manuel Auteurs : Mitsunori Ogihara, Auteur Editeur : Springer Nature Année de publication : 2018 Importance : 515 p. Format : 18 x 26 cm ISBN/ISSN/EAN : 978-3-319-89491-1 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Langages informatiques
[Termes IGN] Java (langage de programmation)
[Termes IGN] programmation informatiqueRésumé : (éditeur) Making extensive use of examples, this textbook on Java programming teaches the fundamental skills for getting started in a command-line environment. Meant to be used for a one-semester course to build solid foundations in Java, Fundamentals of Java Programming eschews second-semester content to concentrate on over 180 code examples and 250 exercises. Key object classes (String, Scanner, PrintStream, Arrays, and File) are included to get started in Java programming. The programs are explained with almost line-by-line descriptions, also with chapter-by-chapter coding exercises. Teaching resources include solutions to the exercises, as well as digital lecture slides Note de contenu : I- Programming Basics
II- Loops
III- Arrays and Objects
IV- Advanced ConceptsNuméro de notice : 25807 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Manuel informatique DOI : 10.1007/978-3-319-89491-1 En ligne : https://doi.org/10.1007/978-3-319-89491-1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95063
Titre : Introduction to Deep Learning : From Logical Calculus to Artificial Intelligence Type de document : Monographie Auteurs : Sandro Skansi, Auteur Editeur : Springer Nature Année de publication : 2018 Importance : 196 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-319-73004-2 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage profond
[Termes IGN] classification
[Termes IGN] codage
[Termes IGN] estimation par noyau
[Termes IGN] matrice de covariance
[Termes IGN] Perceptron multicouche
[Termes IGN] Python (langage de programmation)
[Termes IGN] régression logistique
[Termes IGN] réseau neuronal artificiel
[Termes IGN] réseau neuronal convolutif
[Termes IGN] sciences cognitives
[Termes IGN] théorie des probabilitésRésumé : (auteur) This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner. The content coverage includes convolutional networks, LSTMs, Word2vec, RBMs, DBNs, neural Turing machines, memory networks and autoencoders. Numerous examples in working Python code are provided throughout the book, and the code is also supplied separately at an accompanying website.
Topics and features:
Introduces the fundamentals of machine learning, and the mathematical and computational prerequisites for deep learning
Discusses feed-forward neural networks, and explores the modifications to these which can be applied to any neural network
Examines convolutional neural networks, and the recurrent connections to a feed-forward neural network
Describes the notion of distributed representations, the concept of the autoencoder, and the ideas behind language processing with deep learning
Presents a brief history of artificial intelligence and neural networks, and reviews interesting
open research problems in deep learning and connectionism
This clearly written and lively primer on deep learning is essential reading for graduate and advanced undergraduate students of computer science, cognitive science and mathematics, as well as fields such as linguistics, logic, philosophy, and psychology.Note de contenu : 1- From Logic to Cognitive Science
2- Mathematical and Computational Prerequisites
3- Machine Learning Basics
4- Feedforward Neural Networks
5- Modifications and Extensions to a Feed-Forward Neural Network
6- Convolutional Neural Networks
7- Recurrent Neural Networks
8- Autoencoders
9- Neural Language Models
10- An Overview of Different Neural Network Architectures
11- ConclusionNuméro de notice : 25787 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/MATHEMATIQUE Nature : Monographie En ligne : https://doi.org/10.1007/978-3-319-73004-2 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94990
Titre : Physics of oscillations and waves Type de document : Monographie Auteurs : Arnt Inge Vistnes, Auteur Editeur : Springer Nature Année de publication : 2018 Importance : 594 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-319-72314-3 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Physique
[Termes IGN] diffraction
[Termes IGN] interférence
[Termes IGN] longueur d'onde
[Termes IGN] onde
[Termes IGN] onde acoustique
[Termes IGN] onde électromagnétique
[Termes IGN] onde lumineuse
[Termes IGN] oscillation
[Termes IGN] phase
[Termes IGN] transformation de Fourier
[Termes IGN] transformation en ondelettesRésumé : (éditeur) In this textbook a combination of standard mathematics and modern numerical methods is used to describe a wide range of natural wave phenomena, such as sound, light and water waves, particularly in specific popular contexts, e.g. colors or the acoustics of musical instruments. It introduces the reader to the basic physical principles that allow the description of the oscillatory motion of matter and classical fields, as well as resulting concepts including interference, diffraction, and coherence. Numerical methods offer new scientific insights and make it possible to handle interesting cases that can’t readily be addressed using analytical mathematics; this holds true not only for problem solving but also for the description of phenomena. Essential physical parameters are brought more into focus, rather than concentrating on the details of which mathematical trick should be used to obtain a certain solution. Readers will learn how time-resolved frequency analysis offers a deeper understanding of the interplay between frequency and time, which is relevant to many phenomena involving oscillations and waves. Attention is also drawn to common misconceptions resulting from uncritical use of the Fourier transform. The book offers an ideal guide for upper-level undergraduate physics students and will also benefit physics instructors. Program codes in Matlab and Python, together with interesting files for use in the problems, are provided as free supplementary material. Note de contenu : 1- Introduction
2- Free and Damped Oscillations
3- Forced Oscillations and Resonance
4- Forced Oscillations and Resonance
5- Fourier Analysis
6- Waves
7- Sound
8- Dispersion and Waves on Water
9- Electromagnetic Waves
10- Reflection, Transmission and Polarization
11- Measurements of Light, Dispersion, Colours
12- Geometric Optics
13- Interference—Diffraction
14- Wavelet Transform
15- Coherence, Dipole Radiation and Laser
16- Skin Depth and WaveguidesNuméro de notice : 25817 Affiliation des auteurs : non IGN Nature : Monographie En ligne : https://doi.org/10.1007/978-3-319-72314-3 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95082
[périodique] Voir les bulletins disponibles Rechercher dans ce périodique
Titre : Applied network science Type de document : Périodique Editeur : Springer Nature Année de publication : 2016 - Langues : Anglais (eng) Résumé : open-access and strictly peer-reviewed journal; indexing by DOAJ, DBLP and Scopus Numéro de notice : 000 Affiliation des auteurs : non IGN Nature : Titre de périodique En ligne : https://appliednetsci.springeropen.com/articles Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98903
[périodique] Voir les bulletins disponibles Rechercher dans ce périodique
Titre : Open Geospatial Data, Software and Standards Type de document : Périodique Editeur : Springer Nature Année de publication : 2016 - ISBN/ISSN/EAN : 2363-7501 Langues : Anglais (eng) Résumé : Titre en open access
(éditeur) Open Geospatial Data, Software and Standards provides an advanced forum for the science and technology of open data, crowdsourced information, and sensor web through the publication of reviews and regular research papers. The journal publishes articles that address issues related, but not limited to, the analysis and processing of open geo-data, standardization and interoperability of open geo-data and services, as well as applications based on open geo-data. The journal is also meant to be a space for theories, methods and applications related to crowdsourcing, volunteered geographic information, as well as Sensor Web and related topics.Numéro de notice : 000 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Titre de périodique En ligne : http://opengeospatialdata.springeropen.com/articles Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84063 Photogrammetric computer vision / Wolfgang Förstner (2016)PermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalink