Libros de Aplicaciones, página 6 de 125

Now even more applied, the eighth edition of Instructional Technology and Media for Learning offers a unique chapter-case framework grounded in the popular ASSURE model. This new, integrated chapter-case framework teaches readers to apply current and future computer, multimedia, Internet/intranet, learning foundations, and audiovisual technologies to classroom instruction. It also assists readers in connecting chapter concepts, Companion Website, and CD-ROM resources to classroom application, guides readers through a full case using the ASSURE model, and provides readers with valuable practice in applying the ASSURE model to create lesson plans for their own classrooms.

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.