Series
In a Minute
Dense, visual breakdowns of complex ML concepts — Transformers, SVMs, CNNs, Forecasting, Bayesian Learning & more. Built to understand the big picture in a single read.
11 articles
- Confidence Interval vs Prediction Interval ExplainedAn interactive simulation showing why CI and PI are fundamentally different things. One band covers the mean line. The other catches new individual points.
- Transformer Architecture Explained in a MinuteTransformer architecture explained in 5 minutes: 6 components, Attention formula, encoder vs decoder, GPT vs BERT. Visual breakdown, no fluff.
- Forecasting in a MinuteWhat is forecasting, why it matters, 6 types, and how inline forecasting works — with a real interest-rate decision model walkthrough.
- SVMs & Kernel Trick ExplainedSupport Vector Machines: optimal hyperplane, support vectors, kernel trick, and 4 kernel types with gamma tuning.
- Scalars, Vectors, Matrices & Tensors ExplainedFrom a single number to n-dimensional arrays — scalars, vectors, matrices, and tensors defined, notated, and compared in one read.
- CNNs Explained — Convolutional Neural Networks in a MinuteCNN architecture in one read: kernel, stride, padding, pooling, flattening — how images flow from pixels to predictions.
- Multiclass vs Multilabel Classification ExplainedBinary, multiclass, and multilabel classification: one label vs many labels, with real examples and when to use each.
- Confusion Matrix & Metrics ExplainedTP, TN, FP, FN, accuracy, precision, recall, F1, specificity — every classification metric defined and applied.
- Bayesian Learning ExplainedBayes theorem, prior, likelihood, posterior — how beliefs update with evidence, with disease diagnosis and horse race examples.
- Linear Regression Explainedy=mx+b, cost function (MSE), gradient descent, R² score, multivariable regression — with Python implementation.
- Support Vector Machine (SVM)SVM: hyperplanes, margins, support vectors, C and gamma tuning, 4 kernels, and Iris dataset implementation with scikit-learn.