Learn With Jay on MSN

Bias vs variance explained: Avoid overfitting in ML

What is overfitting and underfitting in machine learning? What is Bias and Variance? Overfitting and Underfitting are two common problems in machine learning and Deep learning. If a model has low ...
Scientific Research Publishing

Multimodal Digital Phenotyping for Bipolar Disorder: Robust Mood-State Classification and Early Relapse Risk Monitoring ()

Bipolar Disorder, Digital Phenotyping, Multimodal Learning, Face/Voice/Phone, Mood Classification, Relapse Prediction, T-SNE, Ablation Share and Cite: de Filippis, R. and Al Foysal, A. (2025) ...

NetraMark and CAMH Announce Research Award to Advance AI-Driven Discovery in Schizophrenia and Depression

NetraMark Holdings Inc. (the “Company” or “NetraMark”) (CSE: AIAI) (OTCQB: AINMF) (Frankfurt: PF0), a premier artificial intelligence (AI) company transforming clinical trials with AI-powered ...
Analytics Insight

Data Science: Building Machine Learning Models, Harvard University

Harvard University presents its eight-week online course through edX, which imparts to students essential knowledge of ...
Science X Network

Advancing scalability, efficiency, and reliability of over-parameterized machine learning models

Science X is a network of high quality websites with most complete and comprehensive daily coverage of the full sweep of science, technology, and medicine news ...
Analytics Insight

10 Most Common Machine Learning Mistakes and Tips to Fix Them

Overview: Machine learning failures usually start before modeling, with poor data understanding and preparation.Clean data, ...
BMJ Open

Study protocol for optimising antipsychotic prescribing among hospitalised patients in the acute care setting in Scotland: a national retrospective cohort study

Introduction Prescribing high-dose antipsychotics is typically reserved for individuals with treatment-resistant severe ...
Scientific Research Publishing

Financial Prediction Models in Banks: Combining Statistical Approaches and Machine Learning Algorithms ()

Felimban, R. (2025) Financial Prediction Models in Banks: Combining Statistical Approaches and Machine Learning Algorithms.
IEEE

Machine Learning Applications in Manufacturing—Challenges, Trends, and Future Directions

The emergence of Industry 4.0 (I4.0) has significantly transformed manufacturing landscapes, introducing interconnected, dynamic, and data-rich environments. This article focuses on the application of ...
Tech Xplore on MSN

Overparameterized neural networks: Feature learning precedes overfitting, research finds

Modern neural networks, with billions of parameters, are so overparameterized that they can "overfit" even random, structureless data. Yet when trained on datasets with structure, they learn the ...