Matematik för datavetare

Winner of the Pulitzer Prize A metaphorical fugue on minds and machines in the spirit of Lewis Carroll Douglas Hofstadter's book is concerned directly with the nature of "maps" or links between formal systems. However, according to Hofstadter, the...

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Recent breakthroughs in AI have not only increased demand for AI products, they've also lowered the barriers to entry for those who want to build AI products. The model-as-a-service approach has transformed AI from an esoteric discipline into a po...

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This book summarizes all the core mathematical topics a typical professional software engineer needs to know. In condensing the various concepts covered in an undergraduate computer science program into a single volume, it provides an excellent st...

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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 mo...

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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 broa...

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Biggs' Discrete Mathematics has been a best-selling textbook since the first and revised editions were published in 1986 and 1990, respectively. This second edition has been developed in response to undergraduate course changes and changes in stud...

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Describes recent achievements and classical results of computational complexity theory, including interactive proofs, PCP, derandomization, and quantum computation. It can be used as a reference, for self-study, or as a beginning graduate textbook...

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This open access book gives a systematic introduction into the spectral theory of differential operators on metric graphs. Main focus is on the fundamental relations between the spectrum and the geometry of the underlying graph. The book has two c...

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"My absolute favorite for this kind of interview preparation is Steven Skiena's The Algorithm Design Manual. More than any other book it helped me understand just how astonishingly commonplace ? graph problems are -- they should be part of every w...

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Design robust graph neural networks with PyTorch Geometric by combining graph theory and neural networks with the latest developments and apps Purchase of the print or Kindle book includes a free PDF eBook Key Features Implement -of-the-art graph ...

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This unique book provides a self-contained exposition of the theory of cellular automata on groups and explores its deep connections with recent developments in geometric and combinatorial group theory, amenability, symbolic dynamics, the algebrai...

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Artificial Intelligence and Causal Inference address the recent development of relationships between artificial intelligence (AI) and causal inference. Despite significant progress in AI, a great challenge in AI development we are still facing is ...

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Deep Learning models are at the core of artificial intelligence research today. It is well known that deep learning techniques are disruptive for Euclidean data, such as images or sequence data, and not immediately applicable to graph-structured d...

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"This is an eminently readable book which an ordinary programmer, unskilled in mathematical analysis and wary of theoretical algorithms, ought to be able to pick up and get a lot out of.." - Steve Summit, author of C Programming FAQs Sedgewick has...

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Discover a unique and fun approach to adopting modern typed functions programming patterns.¿This book uses playful metaphors and examples to help you learn Haskell through imagination, building on math without relying on imperative crutches or tec...

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This book is aimed at two kinds of readers: firstly, people working in or near mathematics, who are curious about continued fractions; and secondly, senior or graduate students who would like an extensive introduction to the analytic theory of con...

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The bible of all fundamental algorithms and the work that taught many of today's software developers most of what they know about computer programming. -Byte, September 1995 ¿ I can't begin to tell you how many pleasurable hours of study and recre...

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Geometric algebra (a Clifford Algebra) has been applied to different branches of physics for a long time but is now being adopted by the computer graphics community and is providing exciting new ways of solving 3D geometric problems. John Vince (a...

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This standard textbook on modern graph theory combines the authority of a classic with the engaging freshness of style that is the hallmark of active mathematics. It covers the core material of the subject, with concise yet complete proofs, while ...

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Learn traditional and cutting-edge machine learning (ML) and deep learning techniques and best practices for time series forecasting, including global forecasting models, conformal prediction, and transformer architectures Key Features Apply ML an...

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This fifth edition continues to improve on the features that have made it the market leader. The text offers a flexible organization, enabling instructors to adapt the book to their particular courses. The book is both complete and careful, and it...

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Until recently B-spline curves and surfaces (NURBS) were principally of interest to the computer aided design community, where they have become the standard for curve and surface description. Today we are seeing expanded use of NURBS in modeling o...

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Computational geometry emerged from the ?eld of algorithms design and analysis in the late 1970s. It has grown into a recognized discipline with its own journals, conferences, and a large community of active researchers. The success of the ?eld as...

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The purpose of this book is to provide the reader with a solid background and understanding of the basic results and methods in probability theory before entering into more advanced courses (in probability and/or statistics). The presentation is f...

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With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a conve...

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Deep learning from the ground up using R and the powerful Keras library! In ¿ Deep Learning with R, Second Edition ¿you will learn: Deep learning from first principles Image classification and image segmentation Time series forecasting Text classi...

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Written by one of the foremost experts in high-performance computing and the inventor of Gustafson's law, Every Bit Counts: Posit Computing explains the foundations of a new way for computers to calculate that saves time, storage, energy, and powe...

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Linear algebra and the foundations of deep learning, together at last! From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra t...

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