143 rezultatov
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Bandit Algorithms(2020) LATTIMORE, TOR (UNIVERSITY OF ALBERTA),SZEPESVARI, CSABA (UNIVERSITY OF ALBERTA)Decision-making in the face of uncertainty is a challenge in machine learning, and the multi-armed bandit model is a common framework to address it. This comprehensive introduction is an excellent reference for established researchers and a resource for gVezava: Trda77,00 € -
Machine Learning Refined(2020) WATT, JEREMY (NORTHWESTERN UNIVERSITY, ILLINOIS),BORHANI, REZA (NORTHWESTERN UNIVERSITY, ILLINOIS),KATSAGGELOS, AGGELOS K. (NORTHWESTERN UNIVERSITY, ILLINOIS)An intuitive approach to machine learning detailing the key concepts needed to build products and conduct research. Featuring color illustrations, real-world examples, practical coding exercises, and an online package including sample code, data sets, lecVezava: Trda105,00 € -
Communicating with Data(2021) ALLCHIN, CARLWith this practical book, subject matter experts will learn ways to develop strong, persuasive points when presenting data to different groups in their organizations.Vezava: Mehka82,59 € -
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: Trda85,75 € -
How AI Is Transforming the Organization(2020) REVIEW, MIT SLOAN MANAGEMENT (PAUL MICHELMAN)Vezava: Mehka32,24 € -
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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: Trda99,75 € -
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: Trda63,96 € -
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: Mehka76,02 € -
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: Trda91,00 € -
Deep Network Design for Medical Image Computing(2022) LIAO, HAOFU (APPLIED SCIENTIST, REKOGNITION AND VIDEO ANALYSIS TEAM, AMAZON WEB SERVICES, INC, CA, USA),ZHOU, S. KEVIN (PRINCIPAL KEY EXPERT, MEDICAL IMAGE ANALYSIS, SIEMENS HEALTHCARE TECHNOLOGY CENTER, PRINCETON, NEW JERSEY, USA),LUO, JIEBO (PROFESSOR OF COMPUTER SCIENCE, UNIVERSITY OF ROCHESTER, NY, USA)Vezava: Mehka129,13 € -
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: Mehka75,24 € -
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: Trda129,71 € -
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: Mehka78,75 € -
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: Trda127,75 € -
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: Mehka23,42 € -
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: Mehka86,94 €
