Account
Orders
Advanced search
Intended for researchers and practitioners in interaction design, this book shows how Bayesian models can be brought to bear on problems of interface design and user modelling. It introduces and motivates Bayesian modelling and illustrates how powerful these ideas can...
The Science of Deep Learning emerged from courses taught by the author that have provided thousands of students with training and experience for their academic studies, and prepared them for careers in deep learning, machine learning, and artificial intelligence in top...
Computer science majors taking a non-programming-based course like discrete mathematics might ask 'Why do I need to learn this?' Written with these students in mind, this text introduces the mathematical foundations of computer science by providing a comprehensive...
As the world becomes increasingly connected, it is also more exposed to a myriad of cyber threats. We need to use multiple types of tools and techniques to learn and understand the evolving threat landscape. Data is a common thread linking various types of devices and...
Probabilistic numerical computation formalises the connection between machine learning and applied mathematics. Numerical algorithms approximate intractable quantities from computable ones. They estimate integrals from evaluations of the integrand, or the path of a...
A high school student can create deep Q-learning code to control her robot, without any understanding of the meaning of 'deep' or 'Q', or why the code sometimes fails. This book is designed to explain the science behind reinforcement learning and optimal control in a...
CUDA is now the dominant language used for programming GPUs, one of the most exciting hardware developments of recent decades. With CUDA, you can use a desktop PC for work that would have previously required a large cluster of PCs or access to a HPC facility. As a...
The latest edition of the definitive guide to the Ada language covers the full details of the core language Ada 2012 as updated by the 2016 ISO Corrigendum and introduces the key new features in Ada 2022.The book is in four parts. It begins by introducing the...
Since the early eighteenth century, the theory of networks and graphs has matured into an indispensable tool for describing countless real-world phenomena. However, the study of large-scale features of a network often requires unrealistic limits, such as taking the...
Data-driven discovery is revolutionizing how we model, predict, and control complex systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and engineers for the next generation of scientific discovery by offering a broad overview of the...
On social media, new forms of communication arise rapidly, many of which are intense, dispersed, and create new communities at a global scale. Such communities can act as distinct information bubbles with their own perspective on the world, and it is difficult for...
Healthcare has recently seen numerous exciting applications of artificial intelligence, industrial engineering, and operations research. This book, designed to be accessible to a diverse audience, provides an overview of interdisciplinary research partnerships that...
This text provides deep and comprehensive coverage of the mathematical background for data science, including machine learning, optimal recovery, compressed sensing, optimization, and neural networks. In the past few decades, heuristic methods adopted by big tech...
Searching for Trust explores the intersection of trust, disinformation, and blockchain technology in an age of heightened institutional and epistemic mistrust. It adopts a unique archival theoretic lens to delve into how computational information processing has...
The Christoffel–Darboux kernel, a central object in approximation theory, is shown to have many potential uses in modern data analysis, including applications in machine learning. This is the first book to offer a rapid introduction to the subject, illustrating the...
This book introduces machine learning for readers with some background in basic linear algebra, statistics, probability, and programming. In a coherent statistical framework it covers a selection of supervised machine learning methods, from the most fundamental (k-NN,...
News headlines about privacy invasions, discrimination, and biases discovered in the platforms of big technology companies are commonplace today, and big tech's reluctance to disclose how they operate counteracts ideals of transparency, openness, and accountability....
This introduction to quantum computing from a classical programmer's perspective is meant for students and practitioners alike. Over 25 fundamental algorithms are explained with full mathematical derivations and classical code for simulation, using an open-source code...
The dramatic increase in computer performance has been extraordinary, but not for all computations: it has key limits and structure. Software architects, developers, and even data scientists need to understand how exploit the fundamental structure of computer...
Discover this multi-disciplinary and insightful work, which integrates machine learning, edge computing, and big data. Presents the basics of training machine learning models, key challenges and issues, as well as comprehensive techniques including edge learning...
Les livres numériques peuvent être téléchargés depuis l'ebookstore Numilog ou directement depuis une tablette ou smartphone.
PDF : format reprenant la maquette originale du livre ; lecture recommandée sur ordinateur et tablette EPUB : format de texte repositionnable ; lecture sur tous supports (ordinateur, tablette, smartphone, liseuse)
DRM Adobe LCP
LCP DRM Adobe
Sign up to get our latest ebook recommendations and special offers