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Machine Learning
Machine Learning: A Constraint-Based Approach, Second Edition provides readers with a refreshing look at the basic models and algorithms of machine learning, with an emphasis on current topics of interest that include neural networks and kernel machines. The book presents the information in a truly unified manner that is based on the notion of learning from environmental constraints. It draws a path towards deep integration with machine learning that relies on the idea of adopting multivalued logic formalisms, such as in fuzzy systems. Special attention is given to deep learning, which nicely fits the constrained-based approach followed in this book.The book presents a simpler unified notion...
Information Technology for Management: Business and Social Issues
This book constitutes revised selected and extended papers presented at track 4 on "Advances in Information Systems and Technologies" of the Conference on Computer Science and Intelligence Systems, FedCSIS 2021, which was held online during September 2–5, 2021. The FedCSIS Track 4 included AIST 2021, DSH 2021, ISM 2021, and KAM 2021. For this track, a total of 30 submissions was received from which a 7 full papers and 1 short papers were accepted for publication in this volume. The papers were organized in topical sections named as follows: Approaches to improving management systems; solutions to social issues; methods for supporting business and society.
Python for Chemists
Programming in Python empowers chemists to apply their domain knowledge to scales unreachable by manual effort. Learning Python is easy, but contextualizing chemical problems in Python is not always obvious. Readers of this primer develop the skill to identify problems in their research for which code may automate operations and scale a large volume of data or calculation. In addition, the authors shorten the time from “learning” to “using” Python through meaningful problem sets in Chapter One.
Mechatronic & Innovative Applications
Present day mechatronic systems are designed with synergistic integration of mechanics, electronics and computer technology to produce intelligent devices for the purpose of solving real-world problems. Crucial requirements for a mechatronic system are robustness and fault tolerance, i.e. it should have the ability to process incomplete, imprecise or uncertain information. Such systems often have to work in collaborative environments while being subjected to adverse conditions yet adhering to strict safety standards. This e-book explains the fundamentals of designing such systems from the first principles and how to embed intelligence into them. Examples in this volume are not restricted to production lines, but extend to extreme safety based systems such as space and underwater robotics, autonomous transportation systems, aviation systems and medical robots. Moreover, this e-book also presents recent developments in the design of innovative and intelligent mechatronic systems, applied to robotics and transportation systems, thereby providing an authoritative support for researchers and professionals having basic knowledge in mechatronics.
An Introduction to Finite Tight Frames
This textbook is an introduction to the theory and applications of finite tight frames, an area that has developed rapidly in the last decade. Stimulating much of this growth are the applications of finite frames to diverse fields such as signal processing, quantum information theory, multivariate orthogonal polynomials, and remote sensing. Featuring exercises and MATLAB examples in each chapter, the book is well suited as a textbook for a graduate course or seminar involving finite frames. The self-contained, user-friendly presentation also makes the work useful as a self-study resource or reference for graduate students, instructors, researchers, and practitioners in pure and applied mathematics, engineering, mathematical physics, and signal processing.
Python for Bioinformatics
Python for Bioinformatics provides a clear introduction to the Python programming language and instructs beginners on the development of simple programming exercises. Important Notice: The digital edition of this book is missing some of the images or content found in the physical edition.
Deep Learning Architectures
This book describes how neural networks operate from the mathematical point of view. As a result, neural networks can be interpreted both as function universal approximators and information processors. The book bridges the gap between ideas and concepts of neural networks, which are used nowadays at an intuitive level, and the precise modern mathematical language, presenting the best practices of the former and enjoying the robustness and elegance of the latter. This book can be used in a graduate course in deep learning, with the first few parts being accessible to senior undergraduates. In addition, the book will be of wide interest to machine learning researchers who are interested in a theoretical understanding of the subject.
Diamond
This book is about ?diamond?, a logic of paradox. In diamond, a statement can be true yet false; an ?imaginary? state, midway between being and non-being. Diamond's imaginary values solve many logical paradoxes unsolvable in two-valued Boolean logic. In this volume, paradoxes by Russell, Cantor, Berry and Zeno are all resolved. This book has three sections: Paradox Logic, which covers the classic paradoxes of mathematical logic, shows how they can be resolved in this new system; The Second Paradox, which relates diamond to Boolean logic and the Spencer?Brown ?modulator?; and Metamathematical Dilemma, which relates diamond to Gdelian metamathematics and dilemma games.
Python Scripting for Computational Science
Scripting with Python makes you productive and increases the reliability of your scientific work. Here, the author teaches you how to develop tailored, flexible, and efficient working environments built from small programs (scripts) written in Python. The focus is on examples and applications of relevance to computational science: gluing existing applications and tools, e.g. for automating simulation, data analysis, and visualization; steering simulations and computational experiments; equipping programs with graphical user interfaces; making computational Web services; creating interactive interfaces with a Maple/Matlab-like syntax to numerical applications in C/C++ or Fortran; and building flexible object-oriented programming interfaces to existing C/C++ or Fortran libraries.
Numerical Python
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