machine learning ↔ deep learning

Relations (1)

related 2.32 — strongly supporting 8 facts

Machine learning and deep learning are closely related as they are both utilized for energy prediction, forecasting, and policy analysis as evidenced by [1] and [2]. Furthermore, they are frequently applied together to capture temporal data patterns in energy systems [3] and to model the impacts of renewable energy integration [4].

Facts (8)

Sources

Global perspectives on energy technology assessment and ... link.springer.com Springer 4 facts

referenceNanjar et al. (2024) performed a systematic literature review of machine learning and deep learning approaches used for energy prediction.

claimMachine learning and deep learning methods, specifically Long Short-term memory (LSTM) and Gated Recurrent unit (GRU), are efficient at capturing and utilizing temporal data sequences and time-series patterns in energy systems.

referenceEl-Azab et al. (2024) evaluated machine learning and deep learning approaches for forecasting electricity prices and assessing energy loads using real datasets.

claimAI can analyze renewable energy policy scenarios, generate models to anticipate long-term impacts of renewable energy integration, and assess climate change risks using machine learning and deep learning functions.

A Comprehensive Review of Neuro-symbolic AI for Robustness ... link.springer.com Springer 1 fact

referenceFakour, Mosleh, and Ramezani published a structured review of literature concerning uncertainty in machine learning and deep learning in 2024.

Enterprise AI Requires the Fusion of LLM and Knowledge Graph stardog.com Stardog 1 fact

claimEnterprise customers require a GenAI stack that is modular, reusable, reproducible, trustworthy, includes lineage and traceability, and decouples machine learning, deep learning, and GenAI tasks while grounding them in quality data.

Understanding LLM Understanding skywritingspress.ca Skywritings Press 1 fact

claimMikhail Belkin, a Professor at the Halicioglu Data Science Institute at the University of California, San Diego, and an Amazon Scholar, researches the theory and applications of machine learning and data analysis, specifically focusing on statistical phenomena in deep learning.

What Is Open Source Software? - IBM ibm.com IBM 1 fact

claimIT professionals commonly deploy open source software in categories including programming languages and frameworks, databases and data technologies, operating systems, Git-based public repositories, and frameworks for artificial intelligence, machine learning, and deep learning.