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Category: AI / Machine Learning
An intuitive explanation of how convolutional operations affect spatial dimensions in image data. Covers kernel size, stride, padding, and output shape calculations — essential knowledge for understanding CNN architecture design.
A concise guide to the core concepts, skills, and mental models every machine learning engineer should internalize — from data preparation and model evaluation to algorithm intuition, production workflows, and engineering mindset.
An in-depth refresher to K-Means clustering — covering its algorithm, mathematical foundations, parameter selection, strengths, and limitations. Learn how this classic unsupervised method groups data based on proximity to centroids.
A practical refresher to DBSCAN, the density-based clustering algorithm that identifies clusters of arbitrary shape and detects outliers without requiring a predefined number of clusters. Includes key math concepts, parameter tuning, and real-world use cases.
A clear and practical overview of the core principles behind classical machine learning — including supervised vs. unsupervised learning, model generalization, and the importance of evaluation techniques.