Libros de Aplicaciones, página 29 de 131

Partiendo del hecho de que la literatura sobre la realidad virtual es muy poco abundante y tiende a describir primordialmente el lado más espectacular y mágico de esta nueva disciplina, la originalidad del presente libro reside ante todo en su vertiente tecnica. En otras palabras: los autores destinan sus razonamientos tanto a los investigadores como a los especialistas, pero su objetivo principal es describir la ultima tecnologia disponible, con sus limites y sus posibilidades. A partir de ahi, abordan los problemas de fondo referidos a las prestaciones y metodos de evaluacion de dichas tecnologias y, finalmente, delimitan con sumo cuidado los campos de investigacion, examinando las potencialidades que presentan y describiendo las restricciones que imponen. El resultado no solo es un libro de agil y estimulante lectura, una fascinante ventana abierta a un universo con inmensas posibilidades de futuro, sino tambien el primer manual cientifico que aborda el tema de forma realmente rigurosa y exhaustiva.

Build real-world time series forecasting systems which scale to millions of time series by applying modern machine learning and deep learning conceptsKey FeaturesExplore industry-tested machine learning techniques used to forecast millions of time seriesGet started with the revolutionary paradigm of global forecasting modelsGet to grips with new concepts by applying them to real-world datasets of energy forecastingBook DescriptionWe live in a serendipitous era where the explosion in the quantum of data collected and a renewed interest in data-driven techniques such as machine learning (ML), has changed the landscape of analytics, and with it, time series forecasting. This book, filled with industry-tested tips and tricks, takes you beyond commonly used classical statistical methods such as ARIMA and introduces to you the latest techniques from the world of ML.This is a comprehensive guide to analyzing, visualizing, and creating state-of-the-art forecasting systems, complete with common topics such as ML and deep learning (DL) as well as rarely touched-upon topics such as global forecasting models, cross-validation strategies, and forecast metrics. You'll begin by exploring the basics of data handling, data visualization, and classical statistical methods before moving on to ML and DL models for time series forecasting. This book takes you on a hands-on journey in which you'll develop state-of-the-art ML (linear regression to gradient-boosted trees) and DL (feed-forward neural networks, LSTMs, and transformers) models on a real-world dataset along with exploring practical topics such as interpretability.