machine learning basics

3 articles in this category.

ML Hyperparameters Explained for Beginners: Learning Rate, Epochs, Batch Size, L2, and Seed

A beginner-friendly explanation of core machine learning hyperparameters — learning rate, epochs, batch size, L2 regularization, and random seed — with simple examples and every important term explained clearly.

April 19, 20269 min read

#machine-learning#hyperparameters#learning-rate

TF-IDF + Logistic Regression: The Classical ML Baseline You Should Try First

intermediateMachine Learning Basics

Before reaching for LLMs or neural networks for text classification, try the boring thing. Here's how TF-IDF + Logistic Regression works, why it's often embarrassingly competitive, and where it breaks.

April 19, 202610 min read

#tf-idf#logistic-regression#sklearn

The Impartial Judge: Inside a Production ML Evaluation Harness

intermediateMachine Learning Basics

A developer's walkthrough of a real ML eval harness — F1, macro averaging, OOS recall, warmup, and p50/p95/p99 latency — and the design decisions behind each.

April 16, 202612 min read

#evaluation#metrics#f1-score