Imagine a teacher grading a multiple-choice exam. If the teacher says, “Only this one answer has any value, and all […]
Label Smoothing: Intuition, Mathematics, Gradients, and Practical Use Read More »
Decision trees are one of the simplest ways to turn data into a sequence of human-readable rules. You can think
How Decision Trees Work: A Practical Machine Learning Guide Read More »
Imagine that you need to open $K$ new coffee shops in a city. You want each person to walk to
Understanding K-Means Clustering: Intuition, Math, and Practical Implementation Read More »
1. The Intuition: The Overconfident Weather Forecaster Imagine planning a weekend picnic. You check two weather applications. You cancel the
Model Calibration: When Your Model’s Confidence Actually Matters Read More »
BART is a sequence-to-sequence (encoder–decoder) Transformer pretrained as a denoising autoencoder: it learns to reconstruct clean text $x$ from a
BART (Bidirectional and Auto-Regressive Transformers) Read More »
RLHF is a post-training recipe for turning a broadly capable language model into a more useful assistant. In practice, it
Reinforcement Learning with Human Feedback (RLHF) Read More »
Imagine you are trying to teach a computer to paint. A classic autoencoder is a skilled copier: it learns an
Variational Autoencoders (VAEs): Intuition, Math, and Practical Implementation Read More »
Imagine a student who has memorized an entire textbook, but only answers questions when they are phrased exactly like the
FLAN-T5: Instruction Tuning for a Stronger “Do What I Mean” Model Read More »
Imagine you are building a house. You could hire one master builder who knows everything about construction, from plumbing and
Mixture of Experts (MoE): Scaling Model Capacity Without Proportional Compute Read More »
Before LightGBM entered the scene, another algorithm reigned supreme in the world of machine learning competitions and industrial applications: XGBoost.
XGBoost: Extreme Gradient Boosting — A Complete Deep Dive Read More »
Autoencoder is a type of neural network architecture designed for unsupervised learning which excel in dimensionality reduction, feature learning, and
Autoencoders in NLP and ML: A Comprehensive Overview Read More »
Imagine a group of hospitals trying to train a disease-risk model together.Each hospital has valuable patient records, but nobody is
Federated Learning: Training Models Where the Data Lives Read More »
