Series · 5 parts

AI, ML, LLMs, and Neural Networks: A Practitioner's Introduction

  1. 01What Machine Learning Actually Is — Before the Hype and After the BuzzwordsA precise definition of machine learning, the difference between supervised, unsupervised, and reinforcement learning, and why understanding the category matters before choosing an approach.
  2. 02Neural Networks Explained: What They Are, How They Learn, and Why They WorkNeurons, layers, weights, activation functions, backpropagation, and gradient descent — the actual mechanics of how a neural network learns from data, explained without the mathematics becoming the obstacle.
  3. 03Large Language Models: What They Are, How They Are Built, and What They Cannot DoTransformers, attention, pretraining, fine-tuning, RLHF — the architecture and training pipeline behind GPT, Claude, Llama, and every other modern LLM, explained for engineers who need to use and evaluate these systems.
  4. 04RAG, Prompting, and Building Applications on LLMs: The Practitioner's GuideRetrieval-augmented generation, system prompts, few-shot examples, chain-of-thought, structured output, and the engineering patterns that make LLM applications reliable in production.
  5. 05Putting AI in Production: What Enterprise AI Deployment Actually RequiresModel serving, latency, cost, monitoring, governance, and the operational discipline that separates a proof of concept from an AI system that runs reliably at enterprise scale.