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Computer Science (R0) Serie de eBooks

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  • Applied Machine Learning

    de David Forsyth ...
    Series series Computer Science (R0)
    Machine learning methods are now an important tool for scientists, researchers, engineers and students in a wide range of areas. This book is written for people who want to adopt and use the main tools of machine learning, but aren’t necessarily going to want to be machine learning researchers. Intended for students in final year undergraduate or first year graduate computer science programs in ... Leer más

    $89.09 USD

  • Probability and Statistics for Computer Science

    de David Forsyth ...
    Series series Computer Science (R0)
    This textbook is aimed at computer science undergraduates late in sophomore or early in junior year, supplying a comprehensive background in qualitative and quantitative data analysis, probability, random variables, and statistical methods, including machine learning.With careful treatment of topics that fill the curricular needs for the course, Probability and Statistics for Computer Science ... Leer más

    $49.99 USD

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  • Math for Deep Learning

    What You Need to Know to Understand Neural Networks

    de Ronald T. Kneusel ...
    Math for Deep Learning provides the essential math you need to understand deep learning discussions, explore more complex implementations, and better use the deep learning toolkits.With Math for Deep Learning, you'll learn the essential mathematics used by and as a background for deep learning.You’ll work through Python examples to learn key deep learning related topics in probability, statistics, ... Leer más

    $29.99 USD

  • System Identification

    Theory for the User

    de Lennart Ljung ...
    The field's leading text, now completely updated.Modeling dynamical systems — theory, methodology, and applications.Lennart Ljung's System Identification: Theory for the User is a complete, coherent description of the theory, methodology, and practice of System Identification. This completely revised Second Edition introduces subspace methods, methods that utilize frequency domain data, and ... Leer más

    $134.99 USD

  • Fundamentals of Deep Learning

    Designing Next-Generation Machine Intelligence Algorithms

    de Nithin Buduma, Nikhil Buduma, Joe Papa ...
    We're in the midst of an AI research explosion. Deep learning has unlocked superhuman perception to power our push toward creating self-driving vehicles, defeating human experts at a variety of difficult games including Go, and even generating essays with shockingly coherent prose. But deciphering these breakthroughs often takes a PhD in machine learning and mathematics.The updated second edition ... Leer más

    $48.99 USD

  • Introduction to Machine Learning, fourth edition

    de Ethem Alpaydin ...
    Series series Adaptive Computation and Machine Learning series
    A substantially revised fourth edition of a comprehensive textbook, including new coverage of recent advances in deep learning and neural networks.The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Machine learning underlies such exciting new technologies as self-driving cars, speech recognition, and translation applications. This ... Leer más

    $52.99 USD

  • Machine Learning

    a Concise Introduction

    de Steven W. Knox ...
    Series Libro 285 - Wiley Series in Probability and Statistics
    **AN INTRODUCTION TO MACHINE LEARNING THAT INCLUDES THE FUNDAMENTAL TECHNIQUES, METHODS, AND APPLICATIONSPROSE Award Finalist 2019Association of American Publishers Award for Professional and Scholarly Excellence**Machine Learning: a Concise Introduction offers a comprehensive introduction to the core concepts, approaches, and applications of machine learning. The author—an expert in the field ... Leer más

    $86.00 USD

  • Applied Geostatistics with SGeMS

    A User's Guide

    de Nicolas Remy, Jianbing Wu, Alexandre Boucher ...
    The Stanford Geostatistical Modeling Software (SGeMS) is an open-source computer package for solving problems involving spatially related variables. It provides geostatistics practitioners with a user-friendly interface, an interactive 3-D visualization, and a wide selection of algorithms. This practical book provides a step-by-step guide to using SGeMS algorithms. It explains the underlying ... Leer más

    $58.99 USD

  • Regularized System Identification

    Learning Dynamic Models from Data

    de Gianluigi Pillonetto, Tianshi Chen, Alessandro Chiuso, Giuseppe De Nicolao, Lennart Ljung ...
    Series series Engineering (R0)
    This open access book provides a comprehensive treatment of recent developments in kernel-based identification that are of interest to anyone engaged in learning dynamic systems from data. The reader is led step by step into understanding of a novel paradigm that leverages the power of machine learning without losing sight of the system-theoretical principles of black-box identification. The ... Leer más

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  • Fundamentals of Machine Learning for Predictive Data Analytics, second edition

    Algorithms, Worked Examples, and Case Studies

    de John D. Kelleher, Brian Mac Namee, Aoife D'Arcy ...
    The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice.Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and ... Leer más

    $49.99 USD

  • Generalized Additive Models

    An Introduction with R, Second Edition

    de Simon N. Wood ...
    Series series Chapman & Hall/CRC Texts in Statistical Science
    The first edition of this book has established itself as one of the leading references on generalized additive models (GAMs), and the only book on the topic to be introductory in nature with a wealth of practical examples and software implementation. It is self-contained, providing the necessary background in linear models, linear mixed models, and generalized linear models (GLMs), before ... Leer más

    $125.99 USD

  • Hands-On Mathematics for Deep Learning

    Build a solid mathematical foundation for training efficient deep neural networks

    de Jay Dawani ...
    A comprehensive guide to getting well-versed with the mathematical techniques for building modern deep learning architecturesKey FeaturesUnderstand linear algebra, calculus, gradient algorithms, and other concepts essential for training deep neural networksLearn the mathematical concepts needed to understand how deep learning models functionUse deep learning for solving problems related to vision, ... Leer más

    $27.99 USD o gratis con Kobo Plus