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Machine learning models improve the prediction of groundwater depth in the Ningxia area of China - Vimarsana News

Machine learning models improve the prediction of groundwater depth in the Ningxia area of China

For the Ningxia area, located in the arid and semi-arid regions of China, groundwater is one of the most important sources of drinking water. However, there has been little research on the application of machine learning models in predicting groundwater in this area.

Source: phys.org
AI Is Making Dams Smarter, More Effective—Here's How - Vimarsana News

AI Is Making Dams Smarter, More Effective—Here's How

A research team delves into the realm of dam management with the help of AI. Employing the power of artificial intelligence, a research team delves into the realm of dam management, revolutionizing the approach to optimize operations and predict behaviors.

HBKU team creates MENA region's first AI match prediction model - Vimarsana News

HBKU team creates MENA region's first AI match prediction model

Doha, Qatar:A Qatari PhD student at Hamad Bin Khalifa University s (HBKU) College of Science and Engineering (CSE), Jassim Mohammed AlMulla and his su...

AI Research Blog - The Transformer Blueprint: A Holistic Guide to the Transformer Neural Network Architecture - Vimarsana News

AI Research Blog - The Transformer Blueprint: A Holistic Guide to the Transformer Neural Network Architecture

A deep dive into Transformer a neural network architecture that was introduced in the famous paper “attention is all you need” in 2017, its applications, impacts, challenges and future directions

The Transformer Family Version 2.0 - Vimarsana News

The Transformer Family Version 2.0

Many new Transformer architecture improvements have been proposed since my last post on “The Transformer Family” about three years ago. Here I did a big refactoring and enrichment of that 2020 post — restructure the hierarchy of sections and improve many sections with more recent papers. Version 2.0 is a superset of the old version, about twice the length. Notations Symbol Meaning $d$ The model size / hidden state dimension / positional encoding size.