Libros de Matemáticas para computación, página 3 de 9

Este texto recoge una colección de problemas resueltos con el programa informático MATLAB, concretamente con la versión 5.3 y revisados con la versión 6.5 de este programa. Las posibilidades de este nuevo software se han utilizado para realizar calculos simbolicos y graficos y esta destinado, tanto a estudiantes como a profesores y profesionales en el ambito de la Ingenieria y Ciencias aplicadas.Las autoras han visto la necesidad de reorganizar y actualizar un material docente que viene a cubrir las necesidades que se han creado en el ambito universitario al integrarse en sus estudios e investigacion la utilizacion de paquetes de software matematico.

Este libro va dirigido a alumnos de Bachillerato, y abarca los curricula de las cuatro asignaturas de este nivel: Matemáticas I y II y Matemáticas aplicadas a las Ciencias Sociales I y II. El libro contiene una práctica de iniciación a DERIVE para Windows en español, treinta y cinco prácticas sobre las materias reseñadas y cuatro más con carácter de ampliación e incluye un disquete con los archivos necesarios para la realización de algunas prácticas.

Este cuaderno está estructurado en cuatro prácticas: Integración múltiple: integral de línea e integración múltiple; Ecuaciones diferenciales; Sistemas de ecuaciones diferencias y Transforma de Lapla

Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning, but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the configuration spaces of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. This text and reference is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

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.

This edition includes new sections on a number of topics including multiple integrals and the modified false position method. There are new, challenging problems drawn from all engineering discipline

This text is designed to teach the basics of numerical analysis to students who need to apply the concepts of numerical computation in their other courses, rather than understand the theoretical basis of the mathematics. Significant changes in this edition include: the change of the FORTRAN code to pseudocode, allowing students to implement the algorithms in whatever language they choose (i.e. C, Pascal, or Fortran.); the text''s incorporation of computer algebra (CAS) programming and algorithms which should give students a better understanding of the differences between the algorithms and the language; more effiecinet algorithms which aim to modernize the text''s presentation.

Authors Cheney and Kincaid show students of science and engineering the modern computer''s potential for solving numerical problems and gives them ample opportunity to hone their skills in programming and problem solving. The text helps students learn about errors that inevitably accompany scientific computing and arms them with methods for detecting, predicting, and controlling these errors. In this edition a discussion of how to locate codes for numerical algorithms on the World Wide Web has been added. A new section on iterative methods for solving large systems of linear equations has also been added.A less scholarly approach and a different menu of topics sets this book apart from the authors'' highly regarded NUMERICAL ANALYSIS: MATHEMATICS OF SCIENTIFIC COMPUTING, SECOND EDITION.

This book provides a well-written and clear introduction to the main topics of modern numerical analysis - sequence of linear equations, error analysis, least squares, nonlinear systems, symmetric eigenvalue problems, three-term recursions, interpolation and approximation, large systems and numerical integrations. It contains a large number of examples and exercises and many figures. This text is suitable for courses in numerical analysis and can also form the basis for a numerical linear algebra course.

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.

Modelización es un libro dedicado a completar el estudio de los modelos matemáticos de optimización iniciado con el texto del mismo autor Programación lineal y entera.La obra se divide en dos partes: el modelo matemático general de optimización estática determinista, conocido habitualmente como modelo de programación no lineal, y los modelos de optimización en redes. La exposición de la programación no lineal se divide en dos apartados. En primer lugar se aborda el problema desde una perspectiva teórica, a fin de identificar qué condiciones debe cumplir la solución de un problema de optimización. Seguidamente, se presentan varias familias de algoritmos para obtener numéricamente dicha solución. El estudio de los modelos de optimización en redes se organiza alrededor de tres grupos de problemas: árboles, caminos y flujos. El esquema de la exposición es similar en los tres casos: se comienza por presentar algunos ejemplos de la situación que se quiere modelar, se analizan los correspondientes algoritmos de solución y se ilustran con ejemplos de su puesta en práctica.