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Machine Learning Fundamentals

This series breaks down complex mathematical theories and algorithms into simple, intuitive explanations with practical examples, making AI accessible to everyone from beginners to aspiring data scientists.
18 articles·~221 min total·Updated
Machine Learning Fundamentals
Ai(17)
  1. Types of LLM Quantization: By Timing, Scope, and Mapping01

    Types of LLM Quantization: By Timing, Scope, and Mapping

    PTQ, QAT, INT8, INT4, and NF4 explained through timing, scope, and mapping choices.

    •14 min read
  2. LLM Model Naming Conventions: How to Read Names and Why They Matter02

    LLM Model Naming Conventions: How to Read Names and Why They Matter

    Learn how to decode LLM names like 8B, Instruct, Q4, and context-window tags.

    •11 min read
  3. LLM Model Quantization: Why, When, and How to Deploy Smaller, Faster Models03

    LLM Model Quantization: Why, When, and How to Deploy Smaller, Faster Models

    Cut GPU memory and latency by converting FP16 weights to INT8 or INT4 — without retraining from scratch.

    •12 min read
  4. LLM Hyperparameters Guide: Temperature, Top-P, and Top-K Explained04

    LLM Hyperparameters Guide: Temperature, Top-P, and Top-K Explained

    Why does ChatGPT sometimes write poetry and sometimes write code? It's all in the settings. We explain how to tune LLMs for creativity vs. precision.

    •14 min read
  5. Mastering Prompt Templates: System, User, and Assistant Roles with LangChain05

    Mastering Prompt Templates: System, User, and Assistant Roles with LangChain

    Robust LLM apps are built with structured messages, not random string concatenation. Learn role-based prompt architecture with LangChain.

    •13 min read
  6. Tokenization Explained: How LLMs Understand Text06

    Tokenization Explained: How LLMs Understand Text

    Computers don't read words; they read numbers. Tokenization is the process of converting text into these numbers. Learn about BPE, WordPiece, and why

    •11 min read
  7. RAG Explained: How to Give Your LLM a Brain Upgrade07

    RAG Explained: How to Give Your LLM a Brain Upgrade

    LLMs hallucinate. RAG fixes that. Learn how Retrieval-Augmented Generation connects ChatGPT to your private data.

    •11 min read
  8. LLM Terms You Should Know: A Helpful Glossary08

    LLM Terms You Should Know: A Helpful Glossary

    A dictionary for the language of Large Language Models. This guide decodes the essential jargon, from Attention to Zero-Shot.

    •13 min read
  9. Mathematics for Machine Learning: The Engine Under the Hood09

    Mathematics for Machine Learning: The Engine Under the Hood

    Don't be scared of the math. We explain Linear Algebra (Data shapes), Calculus (Learning), and Probability (Uncertainty) simply.

    •12 min read
  10. Ethics in AI: Bias, Safety, and the Future of Work11

    Ethics in AI: Bias, Safety, and the Future of Work

    AI is powerful, but is it fair? We explore the critical issues of algorithmic bias, safety alignment, and the economic impact of automation.

    •12 min read
  11. Large Language Models (LLMs): The Generative AI Revolution12

    Large Language Models (LLMs): The Generative AI Revolution

    From GPT-3 to GPT-4. How scaling up simple text prediction created emergent intelligence.

    •13 min read
  12. Natural Language Processing (NLP): Teaching Computers to Read13

    Natural Language Processing (NLP): Teaching Computers to Read

    From Bag of Words to Transformers. A history of how machines learned to understand human language.

    •13 min read
  13. Deep Learning Architectures: CNNs, RNNs, and Transformers14

    Deep Learning Architectures: CNNs, RNNs, and Transformers

    Choosing the right deep learning architecture matters more than model size. Learn when to use CNNs, RNNs, and Transformers.

    •11 min read
  14. Neural Networks Explained: From Neurons to Deep Learning15

    Neural Networks Explained: From Neurons to Deep Learning

    How do computers learn? We start with a single neuron (Perceptron) and build up to Deep Neural Networks.

    •12 min read
  15. Unsupervised Learning: Clustering and Dimensionality Reduction Explained16

    Unsupervised Learning: Clustering and Dimensionality Reduction Explained

    Unsupervised learning finds structure in unlabeled data using clustering and dimensionality reduction techniques.

    •10 min read
  16. Supervised Learning Algorithms: A Deep Dive into Regression and Classification17

    Supervised Learning Algorithms: A Deep Dive into Regression and Classification

    Supervised learning maps inputs to known labels. This advanced guide covers regression, classification, optimization, and deployment trade-offs.

    •12 min read
  17. Machine Learning Fundamentals: A Beginner-Friendly Guide to AI Concepts18

    Machine Learning Fundamentals: A Beginner-Friendly Guide to AI Concepts

    What is the difference between AI, ML, and Deep Learning? We break down the jargon and explain Supervised vs. Unsupervised learning.

    •13 min read
Advanced Ai(1)
  1. Advanced AI: Agents, RAG, and the Future of Intelligence10

    Advanced AI: Agents, RAG, and the Future of Intelligence

    Is an LLM a brain in a jar? To make it truly useful, we need to give it access to the world. This guide explains RAG and Agents.

    •14 min read