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Computer Science (R0) eBook Series

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  • Coding Ockham's Razor

    by Lloyd Allison ...
    Series series Computer Science (R0)
    This book explores inductive inference using the minimum message length (MML) principle, a Bayesian method which is a realisation of Ockham's Razor based on information theory. Accompanied by a library of software, the book can assist an applications programmer, student or researcher in the fields of data analysis and machine learning to write computer programs based upon this principle.MML ... Read more

    $89.09 USD

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  • Math for Deep Learning

    What You Need to Know to Understand Neural Networks

    by 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, ... Read more

    $29.99 USD

  • Machine Learning Algorithms

    A reference guide to popular algorithms for data science and machine learning

    by Giuseppe Bonaccorso ...
    Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guideKey Features\[\*\] Get started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide.\[\*\] Your one-stop solution for everything that matters in mastering the whats and whys of Machine Learning algorithms and their ... Read more

    $41.39 USD or Free with Kobo Plus

  • Fundamentals of Deep Learning

    Designing Next-Generation Machine Intelligence Algorithms

    by 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 ... Read more

    $48.99 USD

  • Regularized System Identification

    Learning Dynamic Models from Data

    by 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 ... Read more

    Free

  • Hands-On Mathematics for Deep Learning

    Build a solid mathematical foundation for training efficient deep neural networks

    by 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, ... Read more

    $27.89 USD or Free with Kobo Plus

  • Probabilistic Machine Learning

    An Introduction

    by Kevin P. Murphy ...
    Series series Adaptive Computation and Machine Learning series
    A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory.This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra ... Read more

    $77.99 USD

  • Hands-On Unsupervised Learning with Python

    Implement machine learning and deep learning models using Scikit-Learn, TensorFlow, and more

    by Giuseppe Bonaccorso ...
    Discover the skill-sets required to implement various approaches to Machine Learning with PythonKey FeaturesExplore unsupervised learning with clustering, autoencoders, restricted Boltzmann machines, and moreBuild your own neural network models using modern Python librariesPractical examples show you how to implement different machine learning and deep learning techniquesBook DescriptionUnsupervised ... Read more

    $35.99 USD or Free with Kobo Plus

  • Learning Bayesian Models with R

    Become an expert in Bayesian Machine Learning methods using R and apply them to solve real-world big data problems

    by Hari Manassery Koduvely ...
    Key FeaturesBook DescriptionBayesian Inference provides a unified framework to deal with all sorts of uncertainties when learning patterns form data using machine learning models and use it for predicting future observations. However, learning and implementing Bayesian models is not easy for data science practitioners due to the level of mathematical treatment involved. Also, applying Bayesian ... Read more

    $27.89 USD or Free with Kobo Plus

  • Computer Aided Verification

    33rd International Conference, CAV 2021, Virtual Event, July 20–23, 2021, Proceedings, Part II

    Edited by Alexandra Silva, K. Rustan M. Leino ...
    Series Book 2 - Springer Nature Proceedings Computer Science
    This open access two-volume set LNCS 12759 and 12760 constitutes the refereed proceedings of the 33rd International Conference on Computer Aided Verification, CAV 2021, held virtually in July 2021.The 63 full papers presented together with 16 tool papers and 5 invited papers were carefully reviewed and selected from 290 submissions. The papers were organized in the following topical sections:Part ... Read more

    Free

  • Computer Aided Verification

    30th International Conference, CAV 2018, Held as Part of the Federated Logic Conference, FloC 2018, Oxford, UK, July 14-17, 2018, Proceedings, Part I

    Edited by Hana Chockler, Georg Weissenbacher ...
    Series Book 1 - Springer Nature Proceedings Computer Science
    This open access two-volume set LNCS 10980 and 10981 constitutes the refereed proceedings of the 30th International Conference on Computer Aided Verification, CAV 2018, held in Oxford, UK, in July 2018.The 52 full and 13 tool papers presented together with 3 invited papers and 2 tutorials were carefully reviewed and selected from 215 submissions. The papers cover a wide range of topics and ... Read more

    Free

  • Gmdh-methodology And Implementation In Matlab

    by Godfrey C Onwubolu ...
    Group method of data handling (GMDH) is a typical inductive modeling method built on the principles of self-organization. Since its introduction, inductive modelling has been developed to support complex systems in prediction, clusterization, system identification, as well as data mining and knowledge extraction technologies in social science, science, engineering, and medicine.This is the first ... Read more

    $38.69 USD