Explains an unsupervised method for tagging search queries using evidence theory and Python, demonstrated with map query examples.
A technical analysis using R to classify iris images from a dataset, applying PCA and LDA for machine learning classification.
Newsletter covering AI, cybersecurity, and tech trends, including analysis of OpenAI's o1 model and a major security incident.
Explores unsupervised methods (k-means and SKATER) for merging homogeneous supercells into larger regions for geospatial data analysis.
An introductory chapter on machine learning and deep learning, covering core concepts, categories, and the shift from traditional programming.
An introductory chapter on machine learning and deep learning, covering core concepts, categories, and terminology from a university course.
Explores self-supervised learning, a method to train models on unlabeled data by creating supervised tasks, covering key concepts and models.
Explores an unsupervised approach combining Mixture of Experts (MoE) with Variational Autoencoders (VAE) for conditional data generation without labels.
Using Python and unsupervised machine learning to analyze Seattle bicycle count data and uncover insights about commuting work habits.
Introduces Stochastic Outlier Selection (SOS), an unsupervised machine learning algorithm for detecting outliers based on affinity between data points.
A technical guide on using K-Means clustering in R to analyze and segment search keywords for understanding user intent in digital analytics.
