Zbirka: Strojno učenje
147 rezultatov
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Deep Learning on Graphs(2021) MA, YAO (MICHIGAN STATE UNIVERSITY),TANG, JILIANG (MICHIGAN STATE UNIVERSITY)This comprehensive text on the theory and techniques of graph neural networks takes students, practitioners, and researchers from the basics to the state of the art. It systematically introduces foundational topics such as filtering pooling, robustness, aVezava: Trda79,71 €
MA, YAO (MICHIGAN STATE UNIVERSITY),TANG, JILIANG (MICHIGAN STATE UNIVERSITY)
Deep Learning on Graphs
This comprehensive text on the theory and techniques of graph neural networks takes students, practitioners, and researchers from the basics to the state of the art. It systematically introduces foundational topics such as filtering pooling, robustness, a
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Reasoning about Uncertainty(2017) HALPERN, JOSEPH Y. (CORNELL UNIVERSITY)Vezava: Mehka89,39 €
HALPERN, JOSEPH Y. (CORNELL UNIVERSITY)
Reasoning about Uncertainty
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Probabilistic Numerics(2022) HENNIG, PHILIPP (EBERHARD-KARLS-UNIVERSITAT TUBINGEN, GERMANY),OSBORNE, MICHAEL A. (UNIVERSITY OF OXFORD),KERSTING, HANS P. (ECOLE NORMALE SUPERIEURE, PARIS)This text provides a first comprehensive introduction to probabilistic numerics, aimed at Masters' and PhD students, as well as postgraduate researchers in artificial intelligence, computer science, statistics, and applied mathematics. It contains extensiVezava: Trda93,27 €
HENNIG, PHILIPP (EBERHARD-KARLS-UNIVERSITAT TUBINGEN, GERMANY),OSBORNE, MICHAEL A. (UNIVERSITY OF OXFORD),KERSTING, HANS P. (ECOLE NORMALE SUPERIEURE, PARIS)
Probabilistic Numerics
This text provides a first comprehensive introduction to probabilistic numerics, aimed at Masters' and PhD students, as well as postgraduate researchers in artificial intelligence, computer science, statistics, and applied mathematics. It contains extensi
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Elements of Causal Inference(2017) PETERS, JONAS (ASSOCIATE PROFESSOR OF STATISTICS, UNIVERSITY OF COPENHAGEN),JANZING, DOMINIK (SENIOR RESEARCH SCIENTIST, MAX PLANCK INSTITUTE FOR INTELLIGENT SYSTEMS),SCHOLKOPF, BERNHARD (DIRECTOR OF THE MAX PLANCK INSTITUTE FOR INTELLIGENT IN TUBINGEN, GERMANY, PROFESSOR FOR MACHINE LEA, MAX PLANCK INSTITUTE FOR INTELLIGENT SYSTEMS)Vezava: Trda61,99 €
PETERS, JONAS (ASSOCIATE PROFESSOR OF STATISTICS, UNIVERSITY OF COPENHAGEN),JANZING, DOMINIK (SENIOR RESEARCH SCIENTIST, MAX PLANCK INSTITUTE FOR INTELLIGENT SYSTEMS),SCHOLKOPF, BERNHARD (DIRECTOR OF THE MAX PLANCK INSTITUTE FOR INTELLIGENT IN TUBINGEN, GERMANY, PROFESSOR FOR MACHINE LEA, MAX PLANCK INSTITUTE FOR INTELLIGENT SYSTEMS)
Elements of Causal Inference
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Mathematics for Machine Learning(2020) DEISENROTH, MARC PETER (UNIVERSITY COLLEGE LONDON),FAISAL, A. ALDO (IMPERIAL COLLEGE LONDON),ONG, CHENG SOONThis self-contained textbook introduces all the relevant mathematical concepts needed to understand and use machine learning methods, with a minimum of prerequisites. Topics include linear algebra, analytic geometry, matrix decompositions, vector calculusVezava: Mehka73,49 €
DEISENROTH, MARC PETER (UNIVERSITY COLLEGE LONDON),FAISAL, A. ALDO (IMPERIAL COLLEGE LONDON),ONG, CHENG SOON
Mathematics for Machine Learning
This self-contained textbook introduces all the relevant mathematical concepts needed to understand and use machine learning methods, with a minimum of prerequisites. Topics include linear algebra, analytic geometry, matrix decompositions, vector calculus
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Data-Driven Science and Engineering(2022) BRUNTON, STEVEN L. (UNIVERSITY OF WASHINGTON),KUTZ, J. NATHAN (UNIVERSITY OF WASHINGTON)Data-driven discovery is revolutionizing how we model, predict, and control complex systems. This text integrates emerging machine learning and data science methods for engineering and science communities. Now with Python and MATLAB®, new chapters on reinVezava: Trda84,79 €
BRUNTON, STEVEN L. (UNIVERSITY OF WASHINGTON),KUTZ, J. NATHAN (UNIVERSITY OF WASHINGTON)
Data-Driven Science and Engineering
Data-driven discovery is revolutionizing how we model, predict, and control complex systems. This text integrates emerging machine learning and data science methods for engineering and science communities. Now with Python and MATLAB®, new chapters on rein
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First Course in Machine Learning(2020) ROGERS, SIMON (UNIVERSITY OF GLASGOW, UNITED KINGDOM),GIROLAMI, MARK (UNIVERSITY COLLEGE LONDON, UNITED KINGDOM)The new edition of this popular, undergraduate textbook has been revised and updated to reflect current growth areas in Machine Learning. The new edition includes three new chapters with more detailed discussion of Markov Chain Monte Carlo techniques, ClaVezava: Mehka73,49 €
ROGERS, SIMON (UNIVERSITY OF GLASGOW, UNITED KINGDOM),GIROLAMI, MARK (UNIVERSITY COLLEGE LONDON, UNITED KINGDOM)
First Course in Machine Learning
The new edition of this popular, undergraduate textbook has been revised and updated to reflect current growth areas in Machine Learning. The new edition includes three new chapters with more detailed discussion of Markov Chain Monte Carlo techniques, Cla
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Machine Learning for Signal Processing(2019) LITTLE, PROF MAX A. (PROFESSOR OF MATHEMATICS, ASTON UNIVERSITY, PROFESSOR OF MATHEMATICS, ASTON UNIVERSITY, ASTON UNIVERSITY, BIRMINGHAM)Describes in detail the fundamental mathematics and algorithms of machine learning (an example of artificial intelligence) and signal processing, two of the most important and exciting technologies in the modern information economy. Builds up concepts graVezava: Trda128,91 €
LITTLE, PROF MAX A. (PROFESSOR OF MATHEMATICS, ASTON UNIVERSITY, PROFESSOR OF MATHEMATICS, ASTON UNIVERSITY, ASTON UNIVERSITY, BIRMINGHAM)
Machine Learning for Signal Processing
Describes in detail the fundamental mathematics and algorithms of machine learning (an example of artificial intelligence) and signal processing, two of the most important and exciting technologies in the modern information economy. Builds up concepts gra
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Machine Learning Fundamentals(2021) JIANG, HUI (YORK UNIVERSITY, TORONTO)This lucid and coherent introduction to supervised machine learning presents core concepts in a concise, logical and easy-to-follow way for readers with some mathematical preparation but no prior exposure to machine learning. Coverage includes widely usedVezava: Mehka72,92 €
JIANG, HUI (YORK UNIVERSITY, TORONTO)
Machine Learning Fundamentals
This lucid and coherent introduction to supervised machine learning presents core concepts in a concise, logical and easy-to-follow way for readers with some mathematical preparation but no prior exposure to machine learning. Coverage includes widely used
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Model-Based Clustering and Classification for Data Science(2019) BOUVEYRON, CHARLES,CELEUX, GILLES,MURPHY, T. BRENDAN (UNIVERSITY COLLEGE DUBLIN),RAFTERY, ADRIAN E. (UNIVERSITY OF WASHINGTON)This accessible but rigorous introduction is written for advanced undergraduates and beginning graduate students in data science, as well as researchers and practitioners. It shows how a statistical framework yields sound estimation, testing and predictioVezava: Trda118,72 €
BOUVEYRON, CHARLES,CELEUX, GILLES,MURPHY, T. BRENDAN (UNIVERSITY COLLEGE DUBLIN),RAFTERY, ADRIAN E. (UNIVERSITY OF WASHINGTON)
Model-Based Clustering and Classification for Data Science
This accessible but rigorous introduction is written for advanced undergraduates and beginning graduate students in data science, as well as researchers and practitioners. It shows how a statistical framework yields sound estimation, testing and predictio
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AI Ethics(2020) COECKELBERGH, MARK (PROFESSOR OF PHILOSOPHY OF MEDIA AND TECHNOLOGY, UNIVERSITY OF VIENNA)An accessible synthesis of ethical issues raised by artificial intelligence that moves beyond hype and nightmare scenarios to address concrete questions.Vezava: Mehka21,30 €
COECKELBERGH, MARK (PROFESSOR OF PHILOSOPHY OF MEDIA AND TECHNOLOGY, UNIVERSITY OF VIENNA)
AI Ethics
An accessible synthesis of ethical issues raised by artificial intelligence that moves beyond hype and nightmare scenarios to address concrete questions.
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Natural Language Processing with Transformers, Revised Edition(2022) TUNSTALL, LEWIS,VON WERRA, LEANDRO,WOLF, THOMASIf you're a data scientist or coder, this practical book -now revised in full color- shows you how to train and scale these large models using Hugging Face Transformers, a Python-based deep learning library.Vezava: Mehka86,51 €
TUNSTALL, LEWIS,VON WERRA, LEANDRO,WOLF, THOMAS
Natural Language Processing with Transformers, Revised Edition
If you're a data scientist or coder, this practical book -now revised in full color- shows you how to train and scale these large models using Hugging Face Transformers, a Python-based deep learning library.
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Statistical Learning with Math and Python(2021) SUZUKI, JOEThis textbook approaches the essence of machine learning and data science by considering math problems and building Python programs. As the preliminary part, Chapter 1 provides a concise introduction to linear algebra, which will help novices read furthVezava: Mehka49,60 €
SUZUKI, JOE
Statistical Learning with Math and Python
This textbook approaches the essence of machine learning and data science by considering math problems and building Python programs. As the preliminary part, Chapter 1 provides a concise introduction to linear algebra, which will help novices read furth
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Artificial Intelligence (WIRED guides)(2021) BURGESS, MATTHEW,WIREDThe past decade has witnessed extraordinary advances in artificial intelligence. He looks at the ways in which it has already brought us everything from voice recognition software to self-driving cars, and explores its potential for further revolutionaryVezava: Mehka13,88 €
BURGESS, MATTHEW,WIRED
Artificial Intelligence (WIRED guides)
The past decade has witnessed extraordinary advances in artificial intelligence. He looks at the ways in which it has already brought us everything from voice recognition software to self-driving cars, and explores its potential for further revolutionary
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