Libros de Matemáticas para computación, página 3 de 9

Los grupos y los grafos se utilizan en múltiples discipinas. La imposición de técnicas computacionales para la resolución de problemas, ha obligado a su estudio como parte de las herramientas matemáticas, que facilitan la traslacion al lenguaje del ordenador de muchos de estos problemas.En este texto encontramos una completa coleccion de programas, fieles traducciones de las tecnicas matematicas habituales, que resuelven, con abundantes ejemplos, la mayoria de problemas basicos que sobre grupos y grafos finitos podemos plantear.

El libro pretende ayudar a los formadores de los niveles primario y secundario a adentrarse en el campo de los métodos de optimización proporcionándole los elementos necesarios para desarrollar por si mismo estos temas a un nivel adecuado al bachillerato. Igualmente se dirige a todos aquellos que deseen tener un conocimiento un poco mas amplio que el adquirido a lo largo de su carrera en los temas relacionados con los modelos de optimizacion.

Recent years have seen the development of powerful tools for verifying hardware and software systems, as companies worldwide realise the need for improved means of validating their products.There is increasing demand for training in basic methods in formal reasoning so that students can gain proficiency in logic-based verification methods. The second edition of this successful textbook addresses both those requirements, by continuing to provide a clear introduction to formal reasoning which is both relevant to the needs of modern computer science and rigorous enough for practical application. Improvements to the first edition have been made throughout, with extra and expanded sections on SAT solvers, existential/universal second-order logic, micro-models, programming by contract and total correctness.The coverage of model-checking has been substantially updated. Further exercises have been added. Internet support for the book includes worked solutions for all exercises for teachers, and model solutions to some exercises for students.

This book covers new and significant research related to the mathematical modelling of engineering and environmental processes, manufacturing, and industrial systems.It includes heat transfer, fluid mechanics, CFD, and transport phenomena; solid mechanics and mechanics of metals; electromagnets and MHD; reliability modelling and system optimization; finite volume, finite element, and boundary element procedures; decision sciences in an industrial and manufacturing context; civil engineering systems and structures; mineral and energy resources; relevant software engineering issues associated with CAD and CAE; and materials and metallurgical engineering.

Developing many of the major, exciting, pre- and post-millennium developments from the ground up, this book is an ideal entry point for graduate students into quantum information theory. Significant attention is given to quantum mechanics for quantum information theory, and careful studies of the important protocols of teleportation, superdense coding, and entanglement distribution are presented. In this new edition, readers can expect to find over 100 pages of new material, including detailed discussions of Bell's theorem, the CHSH game, Tsirelson's theorem, the axiomatic approach to quantum channels, the definition of the diamond norm and its interpretation, and a proof of the ChoiKraus theorem. Discussion of the importance of the quantum dynamic capacity formula has been completely revised, and many new exercises and references have been added. This new edition will be welcomed by the upcoming generation of quantum information theorists and the already established community of classical information theorists.

Cellular automata have been used in computer science since the 1940s. A cellular automaton consists of many identical simple processing units that interact with each other in a local way and in discrete time. Cellular automata have been applied to a wide variety of tasks and, due to the growing interest of the scientific community, a good number of relevant journals and conferences are dedicated to their study and development. One of the main characteristics of cellular automata is their simplicity, which makes them relatively easy to program. Nonetheless, despite their simplicity, cellular automata are able to generate complex results in many domains, even in the context of artificial life. Another remarkable characteristic of cellular automata is that they are suitable for parallel implementation. The present book deals with the fundamentals of cellular automata. Additionally, a new approach to extending cellular automata with the use of message passing is introduced. Each of the seven chapters of this book includes a number of figures, bibliographic references, and exercises of interest to the reader. The book offers students, practitioners and researchers a concise but broad coverage of the main aspects of cellular automata. The author of the book is an associate professor in the Department of Artificial Intelligence at UNED (Spanish Open University). Since the middle 1990s, he has performed teaching and research activities within the field of artificial intelligence, mainly in the areas of Bayesian networks and evolutionary computation. This book is the result of a long journey that started when the author became interested in NetLogo. NetLogo is an agent]based programming environment well suited for modeling and inspecting complex systems developing over time.

Libro que incluye una colección de 1500 preguntas y ejercicios resueltos. Dirigido a alumnos de informática que imparten la asignatura de matemática discreta y alumnos de la titulación de matemáticas.

Get to grips with deep learning techniques for building image processing applications using PyTorch with the help of code notebooks and test questionsKey FeaturesImplement solutions to 50 real-world computer vision applications using PyTorchUnderstand the theory and working mechanisms of neural network architectures and their implementationDiscover best practices using a custom library created especially for this bookBook DescriptionDeep learning is the driving force behind many recent advances in various computer vision (CV) applications. This book takes a hands-on approach to help you to solve over 50 CV problems using PyTorch1.x on real-world datasets.Youll start by building a neural network (NN) from scratch using NumPy and PyTorch and discover best practices for tweaking its hyperparameters. Youll then perform image classification using convolutional neural networks and transfer learning and understand how they work. As you progress, youll implement multiple use cases of 2D and 3D multi-object detection, segmentation, human-pose-estimation by learning about the R-CNN family, SSD, YOLO, U-Net architectures, and the Detectron2 platform. The book will also guide you in performing facial expression swapping, generating new faces, and manipulating facial expressions as you explore autoencoders and modern generative adversarial networks. Youll learn how to combine CV with NLP techniques, such as LSTM and transformer, and RL techniques, such as Deep Q-learning, to implement OCR, image captioning, object detection, and a self-driving car agent. Finally, youll move your NN model to production on the AWS Cloud.By the end of this book, youll be able to leverage modern NN architectures to solve over 50 real-world CV problems confidently.What You Will LearnTrain a NN from scratch with NumPy and PyTorch Implement 2D and 3D multi-object detection and segmentation Generate digits and DeepFakes with autoencoders and advanced GANs Manipulate images using CycleGAN, Pix2PixGAN, StyleGAN2, and SRGAN Combine CV with NLP to perform OCR, image captioning, and object detection Combine CV with reinforcement learning to build agents that play pong and self-drive a car Deploy a deep learning model on the AWS server using FastAPI and Docker Implement over 35 NN architectures and common OpenCV utilitiesWho this book is forThis book is for beginners to PyTorch and intermediate-level machine learning practitioners who are looking to get well-versed with computer vision techniques using deep learning and PyTorch. If you are just getting started with neural networks, youll find the use cases accompanied by notebooks in GitHub present in this book useful. Basic knowledge of the Python programming language and machine learning is all you need to get started with this book.

This largely self-contained book on the theory of quantum information focuses on precise mathematical formulations and proofs of fundamental facts that form the foundation of the subject. It is intended for graduate students and researchers in mathematics, computer science, and theoretical physics seeking to develop a thorough understanding of key results, proof techniques, and methodologies that are relevant to a wide range of research topics within the theory of quantum information and computation. The book is accessible to readers with an understanding of basic mathematics, including linear algebra, mathematical analysis, and probability theory. An introductory chapter summarizes these necessary mathematical prerequisites, and starting from this foundation, the book includes clear and complete proofs of all results it presents. Each subsequent chapter includes challenging exercises intended to help readers to develop their own skills for discovering proofs concerning the theory of quantum information.

La obra presenta un estudio detallado y sistemático del diseño y análisis de algoritmos. El objetivo de este libro es proporcionar al lector las herramientas básicas que necesitará para desarrollar sus propios algoritmos, cualquiera que sea su campo de aplicación. Se desarrollan las técnicas fundamentales que se utilizan para diseñar y analizar algoritmos eficientes y se ilustran mediante ejemplos concretos de algoritmos tomados de aplicaciones, tales como: la optimización, el álgebra lineal, la criptografía, la teoría computacional de números, la teoría de grafos, la investigación operativa, la inteligencia artificial y otras.

To succeed in data science you need some math proficiency. But not just any math. This common-sense guide provides a clear, plain English survey of the math you'll need in data science, including probability, statistics, hypothesis testing, linear algebra, machine learning, and calculus. Practical examples with Python code will help you see how the math applies to the work you'll be doing, providing a clear understanding of how concepts work under the hood while connecting them to applications like machine learning. You'll get a solid foundation in the math essential for data science, but more importantly, you'll be able to use it to: Recognize the nuances and pitfalls of probability math Master statistics and hypothesis testing (and avoid common pitfalls) Discover practical applications of probability, statistics, calculus, and machine learning Intuitively understand linear algebra as a transformation of space, not just grids of numbers being multiplied and added Perform calculus derivatives and integrals completely from scratch in Python Apply what you've learned to machine learning, including linear regression, logistic regression, and neural networks