AZoBuild

Machine Learning Improves Strength Predictions in Concrete

Supervised machine learning improves predictions of compressive strength in industrial waste-modified concrete, supporting ...
AlphaGalileo

Transfer learning boosts leaf trait estimation from space

Leaf functional traits—chlorophyll (CHL), carotenoid (CAR), equivalent water thickness (EWT), nitrogen (N), and leaf mass per ...
AHA/ASA Journals

Contrastive Machine Learning to Quantify Hypertensive Multiorgan Damage and Identify New Disease Phenotypes: A Multinational Multimodal Study

BACKGROUND: Hypertension induces structural and functional damage in multiple organs. Evidence of subclinical damage ...
IEEE

Efficient Training Management for Mobile Crowd-Machine Learning: A Deep Reinforcement Learning Approach

Abstract: In this letter, we consider the concept of mobile crowd-machine learning (MCML) for a federated learning model. The MCML enables mobile devices in a mobile network to collaboratively train ...

Best Machine Learning Bootcamps

In the table below, we show the main strength, tuition and program length for the best machine learning bootcamps. In this ...
gc.cuny

Computer Science

Graduates of the CUNY Graduate Center's Ph.D. program in Computer Science become masters of the computer science discipline and obtain in-depth knowledge of a specialized area. CUNY Graduate Center Ph ...
GitHub

Machine Learning Engineering Open Book

This is an open collection of methodologies, tools and step by step instructions to help with successful training and fine-tuning of large language models and multi-modal models and their inference.
IEEE

A Machine Learning Approach on SMOS Thin Sea Ice Thickness Retrieval

Abstract: This study proposes a machine learning based methodology for estimating Arctic thin sea ice thickness (up to 1 m) from brightness temperature measurements of SMOS. The approach involves ...
Exploratorium

The Tinkering Studio

Experiment with science, art, technology, and delightful ideas. The Tinkering Studio is primarily an R&D laboratory, so whenever possible we try to share our projects, explorations, and developing ...
Aeon

Learn from machine learning

On good days, the world seems like a well-run railway: things happen according to principles, laws, rules and generalisations that we humans understand and can apply to particulars. We forgive the ...
Nature

Metadynamics sampling in atomic environment space for collecting training data for machine learning potentials

The universal mathematical form of machine-learning potentials (MLPs) shifts the core of development of interatomic potentials to collecting proper training data. Ideally, the training set should ...
Journal of Medical Internet Research

Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language ...

Using a machine-learning, data-driven algorithm, we identified that a combination of age, fever, and tachypnea was the most parsimonious predictor of ICU admission; patients younger than 56 years, ...