Libros de Matemáticas para computación, página 2 de 9

Este libro tiene como objetivo abarcar la totalidad de los temas básicos y avanzados que formulan la base de construcción de los sistemas actuales de procesamiento de imagen. En esta obra se presenta un tratamiento de los temas en dos direcciones. Primero, en la teoría, dando profundidad a los conceptos y algoritmos presentados, al grado que el lector pueda predecir los resultados que se obtendrán si decide modificar parámetros estructurales de los algoritmos. Segundo, en la práctica, aportando la información necesaria para que cada uno de los algoritmos tratados puedan ser implementados por el lector usando la herramienta MatLAB & Simulink. Una característica muy importante de esta obra es que la explicación e implementación de los algoritmos no se reduce solo al uso tradicional de imágenes estáticas procesadas por segmentos de código en MatLAB, sino que enseña la manera de implementar los algoritmos tratados, en tiempo real, mediante la utilización de la herramienta llamada "Video and Image Processing Blockset", que opera sobre el entorno Simulink. El libro a pesar de haber sido estructurado para cubrir la temática de cursos formales universitarios, no se encuentra restringido a ello, por lo que puede ser usado de manera individual por ingenieros, profesionales e investigadores, cubriendo su contenido primero en su sección básica y después en sus temas avanzados, o bien simplemente analizando un tema en particular, aprovechando el carácter auto-contenido de la obra.

Este libro ofrece al lector la información necesaria para abordar la resolución de problemas de cálculo simbólico y gráfico a través de MAPLE, con un desarrollo progresivo de contenidos que le permitirán dominar el entorno de trabajo de este programa, su funcionamiento y sobre todo las posibilidades que ofrece. La exposición es eminentemente práctica para facilitar el conocimiento de las distintas funciones y comandos y, sobre todo, su aplicación en la resolución de problemas. Comenzando por las operaciones elementales con MAPLE, el texto expone los distintos comandos disponibles para trabajar con polinomios, funciones, análisis, resolución de ecuaciones e inecuaciones, cálculo numérico, tratamiento de datos, álgebra lineal y, por supuesto, representación de funciones en 2-D y en 3-D. El estudio y representación de los diferentes tipos de funciones se realiza con especial atención a las opciones que ofrece MAPLE para manipular los gráficos y para modificar su aspecto para adaptarlo a las necesidades del usuario. Cada uno de los capítulos incluye numerosos ejemplos resueltos paso a paso, indicando las instrucciones y comandos ejecutados, mostrando además, las imágenes capturadas del programa en las que se muestran los resultados obtenidos. Además, al final de cada uno de los capítulos se ha incluido una relación de actividades propuestas para que el lector pueda afianzar los conocimientos adquiridos.

Adiabatic quantum computation (AQC) is an alternative to the better-known gate model of quantum computation. The two models are polynomially equivalent, but otherwise quite dissimilar: one property that distinguishes AQC from the gate model is its analog nature. Quantum annealing (QA) describes a type of heuristic search algorithm that can be implemented to run in the "native instruction set" of an AQC platform. D-Wave Systems Inc. manufactures {quantum annealing processor chips} that exploit quantum properties to realize QA computations in hardware. The chips form the centerpiece of a novel computing platform designed to solve NP-hard optimization problems. Starting with a 16-qubit prototype announced in 2007, the company has launched and sold increasingly larger models: the 128-qubit D-Wave One system was announced in 2010 and the 512-qubit D-Wave Two system arrived on the scene in 2013. A 1,000-qubit model is expected to be available in 2014. This monograph presents an introductory overview of this unusual and rapidly developing approach to computation.We start with a survey of basic principles of quantum computation and what is known about the AQC model and the QA algorithm paradigm. Next we review the D-Wave technology stack and discuss some challenges to building and using quantum computing systems at a commercial scale. The last chapter reviews some experimental efforts to understand the properties and capabilities of these unusual platforms. The discussion throughout is aimed at an audience of computer scientists with little background in quantum computation or in physics.s

Focuses on a recent approach to adiabatic perturbation theory, which emphasizes the role of effective equations of motion and the separation of the adiabatic limit from the semiclassical limit. A detailed introduction gives an overview of the subject and makes the later chapters accessible also to readers less familiar with the material. Although the general mathematical theory based on pseudodifferential calculus is presented in detail, there is an emphasis on concrete and relevant examples from physics. Applications range from molecular dynamics to the dynamics of electrons in a crystal and from the quantum mechanics of partially confined systems to Dirac particles and nonrelativistic QED.

Este texto comienza con álgebra básica y muestra cómo se utiliza MATLAB para rsolver problemas de ingeniería en un amplio rango de disciplinas. Los ejemplos desarrollan los conceptos fundamentales de química y física, así como de Ingeniería. A lo largo de toda la obra, se utiliza de forma consistente una metodología estándar para resolver problemas. MATLAB es un poderoso lenguaje de programación que incluye los conceptos comunes a la mayoría de los lenguajes de programación. Puesto que se trata de un lenguaje con base en scripts, la creación de programas y su depuración en MATLAB con frecuencia es más fácil que en los lenguajes de programación tradicionales como C++. Esto hace que MATLAB sea una valiosa herramienta para los cursos introductorios de programación.

El objetivo de este libro es dar apoyo a la docencia de elementos básicos de la lógica matemática que normalmente se incluyen en los planes de estudio típicos de titulaciones en informática.

Este libro pretende ser un complemento práctico a la extensa bibliografía teórica en el área de Diseño Lógico, Estructura y Arquitectura de Computadores. En estas asignaturas el alumno se enfrenta a un gran numero de metodos y conceptos, que naturalmente debe aprender, pero cuya finalidad es ser aplicados en problemas reales. Por ello, creemos que disponer de una coleccion de casos practicos, presentados en forma de ejercicios, puede aclarar los conceptos que se estan estudiando en estas asignaturas.

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

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.

Language Engineering examines the processes involved in dictionary-making, parsing, spell-checking, lexical semantics, machine translation, question answering and text attribution. The dictionary-building and natural language processing techniques examined are applicable to most languages, and the book includes extensive examples in English, French and German.This book is the first detailed description of morphology, syntax and semantics using the C(C++) programming language, and as such should be essential reading for researchers in natural language processing and computational linguistics.

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.

Engineering Analysis with SOLIDWORKS Simulation 2024 goes beyond the standard software manual. Its unique approach concurrently introduces you to the SOLIDWORKS Simulation 2024 software and the fundamentals of Finite Element Analysis (FEA) through hands-on exercises. A number of projects are presented using commonly used parts to illustrate the analysis features of SOLIDWORKS Simulation. Each chapter is designed to build on the skills, experiences and understanding gained from the previous chapters.

Thermal Analysis with SOLIDWORKS Simulation 2022 goes beyond the standard software manual. It concurrently introduces the reader to thermal analysis and its implementation in SOLIDWORKS Simulation using hands-on exercises. A number of projects are presented to illustrate thermal analysis and related topics. Each chapter is designed to build on the skills and understanding gained from previous exercises. Thermal Analysis with SOLIDWORKS Simulation 2022 is designed for users who are already familiar with the basics of Finite Element Analysis (FEA) using SOLIDWORKS Simulation or who have completed the book Engineering Analysis with SOLIDWORKS Simulation 2022. Thermal Analysis with SOLIDWORKS Simulation 2022 builds on these topics in the area of thermal analysis. Some understanding of FEA and SOLIDWORKS Simulation is assumed.