Category · 5 articles
ML Foundations
The core concepts every ML practitioner needs. Covers linear algebra, calculus, probability, statistics, what vectors and embeddings really are, and the structure of the latent spaces that models learn.
What Are Embeddings? How AI Converts Text Into Numbers
A research-backed explanation of what embeddings are in machine learning. Learn how AI models convert text, images, and audio into numerical vectors, how transformer-based embedding models work, and how embeddings power semantic search, RAG pipelines, and vector databases.
Latent Space Explained: The Hidden Structure of AI Models
A research-backed explanation of latent space in AI and machine learning. Learn what latent space is, how models learn it, why its geometry encodes meaning, how vector arithmetic works inside it, and why understanding it makes you a better AI engineer.
What Is a Vector in Machine Learning? Simple Explanation
A clear, research-backed explanation of what vectors are in machine learning. Learn how they represent data, how vector operations like dot product and cosine similarity work, and why they are the foundation of every ML model and AI application you use.
Probability and Statistics: What AI Actually Means by Confidence
Before you can understand how LLMs train or why they hallucinate, you need probability and statistics. This article covers distributions, entropy, Bayes' theorem, and cross-entropy loss with real code examples - the exact concepts that show up in neural network training.
Linear Algebra and Calculus: The Math Your AI Model Runs On
Before you build LLMs or AI products, you need to understand the math underneath. This article breaks down vectors, matrices, dot products, derivatives, and gradients - the exact operations that run inside every neural network.