Thoughts, tutorials, and insights on machine learning, scientific computing, and research.Last updated: 2025-05-17 by linhduongtuan
Comprehensive guide to modern astronomical data analysis techniques, including exoplanet detection algorithms, stellar classification using machine learning, and gravitational wave signal processing.
Advanced computational methods for analyzing fMRI data, including signal processing techniques, connectivity analysis, and machine learning applications in neuroscience research.
Comprehensive analysis of global climate data using machine learning techniques, featuring interactive visualizations, time-series forecasting, and statistical modeling of temperature anomalies.
Exploring the latest advances in deep learning techniques for medical image analysis, including CNN architectures, attention mechanisms, and transformer models for radiology, pathology, and clinical diagnosis.
Exploring how quantum computing algorithms are revolutionizing pharmaceutical research, molecular simulation, and drug optimization with real-world applications and mathematical foundations.
Deep dive into how transformer models are revolutionizing genomic analysis, from DNA sequence classification to protein structure prediction, with mathematical foundations and practical implementations.
Comprehensive guide to creating compelling data visualizations for scientific research, covering mathematical foundations, interactive plotting libraries, and best practices for communicating complex scientific findings.
Leveraging EfficientNet and advanced data augmentation techniques to achieve 95.8% accuracy in TB detection from chest radiographs.
Exploring Vision Transformers (ViTs) for automated cancer detection in whole slide images with attention mechanism visualization.
Novel GNN architecture combining imaging, genomics, and clinical data for personalized cancer treatment prediction.
An in-depth look at how transformer models have revolutionized NLP tasks and how they work under the hood.
Exploring how ML is being used to improve patient outcomes and healthcare efficiency across various domains.
A comprehensive guide to setting up a Python environment for scientific computing and data analysis.
A comprehensive test of all scientific content features including tables, math, figures, and embedded content.