Libros de Matemáticas para computación, página 3 de 9

El texto aborda el estudio en profundidad de los esquemas algorítimicos y de las estructuras de datos desde una perspectiva didáctica, orientada a los estudios de grado en Informática, incluyendo orientaciones para profesores y esquema de dependencias para facilitar el seguimiento y el estudio por parte del alumno. Entre los esquemas algoritmicos incluidos estan el vozar, divide y venceras, programacion dinamica, vuelta atras y ramificacion y poda. Cada capitulo se estructura con un desarrollo del tema que aborda seguido de ejercicios resueltos, propuestos y de notas bibliograficas. El texto combina el estudio de la algoritmica con las estructuras de datos avanzadas que permiten la implementacion de los algoritmos estudiados, como los grafor, monticulos y las tablas hash.

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.

Este volumen trata uno de los tópicos de mayor interés en el área de las matemáticas aplicadas, como es el estudio de las condiciones de optimalidad en la programación matemática.

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.

Este libro estudia la técnicas clásicas de optimización numérica monodimensional y multidimensionales como el Down Hill Simplex. También se ven métodos para funciones con derivadas disponibles, así como metodo para la resolucion de ecuaciones lineales y no-lineales, minimos cuadrados, etc. Los aspectos mas relevantes de la Programacion Lineal y/o entera son tambien tratados, donde se hace un especial en diferentes modelos para solucionar problemas convencionales de transporte, asignacion y/o de redes. Se estudias las tecnicas de Programacion Dinamica haciendo especial enfasis en los algoritmos para la optimizacion de rutas. Finalmente, se describiran los fundamentos de los algoritmos geneticos.

Es tracten aspectes clàssics del control òptim com LQR per a obtindre una llei de control en realimentació de l´estat per a sistemes lineals(continus o discrets) que minimitza un índex quadràtic, sub

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.