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Showing 20 of 22 academic articles
D. V. Lindley • Cambridge University Press eBooks
(1965)
The two parts of this book treat probability and statistics as mathematical disciplines and with the same degree of rigour as is adopted for other branches of applied mathematics at the level of a British honours degree. They contain the mi...
OPENALEX
DOI: https://doi.org/10.1017/cbo9780511662973
F. S. Aghaei Maybodi, M. H. Heydari, F. M. Maalek Ghaeini, M. H. Akrami • پژوهشهای ریاضی
(2021)
Introduction
Many mathematical formulations of physical phenomena contain integro-differential equations. These equations arise in fluid dynamics, biological models, chemical kinetics, ecology, control theory of financial mathematics, aeros...
DOAJ
Open Access (DOAJ)
Younsang Cho, Jaeoh Kim, Donghyeon Yu • IEEE Access
(2022)
A general-purpose GPU (GPGPU) is employed in a variety of domains, including accelerating the spread of deep natural network models; however, further research into its effective implementation is needed. When using the compute unified devic...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/ACCESS.2022.3175987
• Choice Reviews Online
(1998)
PART A: ORDINARY DIFFERENTIAL EQUATIONS (ODE'S). Chapter 1. First-Order ODE's. Chapter 2. Second Order Linear ODE's. Chapter 3. Higher Order Linear ODE's. Chapter 4. Systems of ODE's Phase Plane, Qualitative Methods. Chapter 5. Series Solut...
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DOI: https://doi.org/10.5860/choice.35-5129
Tsuneo Yoshikawa • The MIT Press eBooks
(1990)
Foundations of Robotics presents the fundamental concepts and methodologies for the analysis, design, and control of robot manipulators. It explains the physical meaning of the concepts and equations used, and it provides, in an intuitively...
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DOI: https://doi.org/10.7551/mitpress/3074.001.0001
Kevin Lynch, Frank C. Park • Cambridge University Press eBooks
(2017)
This introduction to robotics offers a distinct and unified perspective of the mechanics, planning and control of robots. Ideal for self-learning, or for courses, as it assumes only freshman-level physics, ordinary differential equations, l...
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DOI: https://doi.org/10.1017/9781316661239
Ronald de Wolf • arXiv (Cornell University)
(2019)
This is a set of lecture notes suitable for a Master's course on quantum computation and information from the perspective of theoretical computer science. The first version was written in 2011, with many extensions and improvements in subse...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1907.09415
Hervé Abdi • Journal of Biological Systems
(1994)
Neural networks are composed of basic units somewhat analogous to neurons. These units are linked to each other by connections whose strength is modifiable as a result of a learning process or algorithm. Each of these units integrates indep...
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DOI: https://doi.org/10.1142/s0218339094000179
Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Cohen, Jonathan et al. • arXiv (Cornell University)
(2014)
We present a library of efficient implementations of deep learning primitives. Deep learning workloads are computationally intensive, and optimizing their kernels is difficult and time-consuming. As parallel architectures evolve, kernels mu...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1410.0759
• Choice Reviews Online
(2011)
The combination of two of the twentieth centurys most influential and revolutionary scientific theories, information theory and quantum mechanics, gave rise to a radically new view of computing and information. Quantum information processin...
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DOI: https://doi.org/10.5860/choice.49-0911
Phillip Kaye, Raymond Laflamme, Michele Mosca • Oxford University Press eBooks
(2006)
This concise, accessible text provides a thorough introduction to quantum computing - an exciting emergent field at the interface of the computer, engineering, mathematical and physical sciences. Aimed at advanced undergraduate and beginnin...
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DOI: https://doi.org/10.1093/oso/9780198570004.001.0001
Mikio Nakahara • CRC Press eBooks
(2008)
Covering both theory and progressive experiments, Quantum Computing: From Linear Algebra to Physical Realizations explains how and why superposition and entanglement provide the enormous computational power in quantum computing. This self-c...
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DOI: https://doi.org/10.1201/9781420012293
Ales Jelinek, Adam Ligocki, Ludek Zalud • Journal of Open Research Software
(2021)
Robotic Template Library (RTL) is a set of tools for dealing with geometry and point cloud processing, especially in robotic applications. The software package covers basic objects such as vectors, line segments, quaternions, rigid transfor...
DOAJ
Open Access (DOAJ)
DOI: 10.5334/jors.353
Richard I. Hartley, Andrew Zisserman • Cambridge University Press eBooks
(2004)
A basic problem in computer vision is to understand the structure of a real world scene given several images of it. Techniques for solving this problem are taken from projective geometry and photogrammetry. Here, the authors cover the geome...
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DOI: https://doi.org/10.1017/cbo9780511811685
Richard Szeliski • Choice Reviews Online
(2011)
Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old ...
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DOI: https://doi.org/10.5860/choice.48-5140
Ian Goodfellow, Yoshua Bengio, Aaron C. Courville • The MIT Press eBooks
(2016)
Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human c...
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DOI: https://doi.org/10.1016/b978-0-12-804291-5.00010-6
Osvaldo Simeone • Cambridge University Press eBooks
(2022)
This self-contained introduction to machine learning, designed from the start with engineers in mind, will equip students with everything they need to start applying machine learning principles and algorithms to real-world engineering probl...
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DOI: https://doi.org/10.1017/9781009072205
Simon Rogers, Mark Girolami • Chapman & Hall/CRC eBooks
(2016)
"A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations ...
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DOI: https://doi.org/10.1201/9781315382159
Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong • Cambridge University Press eBooks
(2020)
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in d...
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DOI: https://doi.org/10.1017/9781108679930
David Barber • Cambridge University Press eBooks
(2012)
Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot ...
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DOI: https://doi.org/10.1017/cbo9780511804779