Libros de Informática práctica, página 41 de 418

La inteligencia de negocios (Business Intelligence) tiene como finalidad esencial extraer el conocimiento contenido en la información. Hoy día se trabaja con gran cantidad de datos y no es fácil analizarlos de modo optimo. El proceso de extraccion del conocimiento a traves de los datos exige el uso de determinadas tecnicas que nos van a permitir extraer perfiles, realizar segmentaciones, modelizar el riesgo, pronosticar ventas, ingresos y otras magnitudes a traves de modelos predictivos y, en definitiva, ofrecer conocimientos para respaldar las decisiones empresariales.Este libro recorre las tecnicas habituales en inteligencia de negocios, asi como las herramientas mas modernas que las implementan, incluyendo las aplicaciones de IBM, SAS y Microsoft. Para cada tecnica se describen someramente los fundamentos metodologicos y se presentan ejemplos practicos resueltos con el software adecuado.

La investigación forense de dispositivos móviles Android es un campo de reciente desarrollo en el que la disponibilidad de conocimientos técnicos, metodologías, herramientas y criterios de actuación estan en proporcion inversa al interes generado hacia los mismos. Esto se debe al vertiginoso desarrollo de este conocido sistema operativo de Google. Smartphones, tabletas, reproductores de medios e incluso electrodomesticos inteligentes Android plantean al investigador problemas dificiles de resolver que no se dan en el analisis forense tradicional de ordenadores de sobremesa y soportes de datos convencionales.La presente obra trata temas de interes relacionados con el analisis forense en dispositivos Android, entre muchos otros:Tecnologia de dispositivos moviles: hardware y software para plataformas Android.Empleo de maquinas virtuales en la investigacion forense de dispositivos moviles Android.Adquisicion forense basada en el empleo del SDK.Rooting y particiones Recovery alternativas.Analisis forense de bases de datos SQLite, archivos XML, aplicaciones, documentos, ejecutables .dex y sistemas de archivos ext4, FAT32 y YAFFS/YAFFS2.Modelo de seguridad Android, delincuencia informatica movil, espionaje industrial y aspectos criminologicos de la investigacion.Soluciones comerciales utilizadas en la investigacion forense de dispositivos moviles.Aplicacion del analisis forense movil en el contexto de la investigacion convencional.

Esta obra nace de la observación de una de las paradojas más llamativas de nuestra praxis jurídico-administrativa, el incuestionable desfase que existe entre el empeño del legislador en proclamar de manera, en ocasiones contundente, el derecho que asiste a los interesados a no aportar al procedimiento documentacion innecesaria o que ya obre de alguna forma en poder de las Administraciones publicas y la escasa aplicacion practica que este derecho ha alcanzado en la experiencia administrativa cotidiana.Se trata de una anomalia que impone a los particulares una carga penosa y con frecuencia evitable que entorpece el funcionamiento armonico de la relacion juridico-administrativa. El enfoque adoptado se centra en los problemas que plantea en la practica la reduccion de la carga documental que soportan los administrados, en la consideracion de esta cuestion desde la perspectiva de la Administracion electronica, asi como en el estudio de las consecuencias juridicas de la vulneracion del derecho a la no aportacion documental y de los mecanismos de defensa con los que cuenta el ciudadano para responder a eventuales infracciones de este derecho.Al margen de la evidente relacion del derecho a la no aportacion de informacion con la simplificacion administrativa en general y con la reduccion de cargas en particular, bajo este asunto subyace una cuestion aun mas sugerente para el jurista. En el fondo de este asunto nos encontramos con un problema de carga de la prueba, dado que del derecho a no facilitar documentacion a la Administracion debe derivarse, al menos en teoria, el de no tener que acreditar ni su existencia ni su contenido ante el organo tramitador

Es un libro completo que incluye todo lo necesario para iniciarse con éxito en el manejo de los PCs y al mismo tiempo dominar los conceptos básicos de la informática. Desde temas generales como el hardware o el software más actual hasta la información detallada sobre el sistema operativo Windows 95 y los comandos del MS-DOS que éste incorpora. Desde lo más fácil a lo más complejo explicado paso a paso para que Ud. lo entienda.

Provides a deep understanding of past, current and future research and applications in educational technology.The handbook''s goal is to enable the reader to gain a deep understanding of past, current and future research and applications in the field of educational technology. The reader will gain an in-depth understanding of complex theories, strategies, concepts, and methods relating to the design, development, implementation, and evaluation of educational technologies.Because it combines both the present experience in the field and the vision of its emerging directions, the handbook will be a comprehensive guide for researchers and practitioners working with educational technologies.All the chapters of the second edition are newly written, the handbook therefore presents the latest developments in the field.

Master advanced techniques and algorithms for deep learning with PyTorch using real-world examplesKey FeaturesUnderstand how to use PyTorch 1.x to build advanced neural network modelsLearn to perform a wide range of tasks by implementing deep learning algorithms and techniquesGain expertise in domains such as computer vision, NLP, Deep RL, Explainable AI, and much moreBook DescriptionDeep learning is driving the AI revolution, and PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch book will help you uncover expert techniques to get the most out of your data and build complex neural network models.The book starts with a quick overview of PyTorch and explores using convolutional neural network (CNN) architectures for image classification. You'll then work with recurrent neural network (RNN) architectures and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation using generative models and explore the world of generative adversarial networks (GANs). You'll not only build and train your own deep reinforcement learning models in PyTorch but also deploy PyTorch models to production using expert tips and techniques. Finally, you'll get to grips with training large models efficiently in a distributed manner, searching neural architectures effectively with AutoML, and rapidly prototyping models using PyTorch and fast.ai.By the end of this PyTorch book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.

THE INSTANT NEW YORK TIMES BESTSELLER AND WORLD ECONOMIC FORUM BOOK CLUB PICK*'A clear, compelling guide to some of the most pressing debates in technology today.' Bill Gates'A colourful and insightful insiders' view of how technology is both empowering us and threatening us. From privacy to cyberattacks, this timely book is a useful guide for how to navigate the digital future.' Walter Isaacson, bestselling author of Steve JobsFrom Microsoft's President and one of the tech industry's wisest thinkers, a frank and thoughtful reckoning with how to balance enormous promise and existential risk as the digitization of everything accelerates. With new chapters on the pandemic and beyond.

A practical guide to understanding and analyzing cyber attacks by advanced attackers, such as nation states.Cyber attacks are no longer the domain of petty criminals. Today, companies find themselves targeted by sophisticated nation state attackers armed with the resources to craft scarily effective campaigns. This book is a detailed guide to understanding the major players in these cyber wars, the techniques they use, and the process of analyzing their advanced attacks. Whether youre an individual researcher or part of a team within a Security Operations Center (SoC), youll learn to approach, track, and attribute attacks to these advanced actors.

The future is now. Acclaimed technologist and inventor Amir Husain explains how we can live amidst the coming age of sentient machines and artificial intelligenceand not only survive, but thrive.Artificial machine intelligence is playing an ever-greater role in our society. We are already using cruise control in our cars, automatic checkout at the drugstore, and are unable to live without our smartphones. The discussion around AI is polarized; people think either machines will solve all problems for everyone, or they will lead us down a dark, dystopian path into total human irrelevance. Regardless of what you believe, the idea that we might bring forth intelligent creation can be intrinsically frightening. But what if our greatest role as humans so far is that of creators?

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