machine learning basics

5 articles in this category.

Phase 2: Agent Architecture — ReAct, Planning, Memory & Frameworks

A comprehensive 8-week deep dive into building AI agents from scratch — ReAct loops, planning patterns, memory systems, and frameworks like LangGraph and AutoGen. Build it yourself before you abstract it away.

May 29, 2026

#agents#react-pattern#langgraph

Phase 1: Core Foundations of LLM Engineering — APIs, Prompts, Tools & RAG

intermediateTutorial

A comprehensive 8-week roadmap covering LLM APIs, prompt engineering, function calling, tool use, and retrieval-augmented generation — everything you need to build production AI applications.

May 29, 2026

#llm#prompt-engineering#rag

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

beginnerMachine Learning Basics

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, 2026

#machine-learning#regularization

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, 2026

#machine-learning#nlp

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, 2026