£119.99

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Springer Deep Learning for Spatio-temporal Data Mining - Big Data

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Description

Deep Learning for Spatio-temporal Data Mining is a comprehensive guide from Springer that explores advanced techniques for extracting patterns from complex, multi-dimensional datasets that evolve over time and space. This Big Data Management resource covers the theoretical foundations and practical applications of deep learning methods specifically designed to handle spatio-temporal data, making it essential reading for data scientists, machine learning engineers, and researchers working with time-series analysis, video processing, climate modelling, traffic prediction, and other dynamic systems. The book bridges the gap between fundamental deep learning concepts and their real-world implementation in large-scale data environments, providing readers with actionable insights into neural network architectures, training strategies, and optimisation techniques tailored for data that contains both spatial and temporal dimensions.

Key Features

Deep Learning for Spatio-temporal Data Mining covers specialised neural network architectures designed specifically for temporal and spatial data analysis.

Springer's Big Data Management series positioning provides expert-level content on handling large-scale complex datasets in production environments.

This book addresses practical applications including video analysis, forecasting, sensor data processing, and geospatial-temporal modelling.

The resource includes both theoretical frameworks and implementation strategies for deploying deep learning solutions on spatio-temporal problems.

Comprehensive coverage of training optimisation techniques for models that process data with both time and location dimensions.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
26 November 2026
Listed Since
06 August 2026

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