Category
data-science
7 articles in this category
Large Language Models (LLMs): The Generative AI Revolution
Introduction: Scale Changes Everything We learned about Transformers in previous posts. An LLM is just a Transformer... but BIG. Big Data: Trained on petabytes of text (books, websites, code). Big Parameters: Hundreds of billions of weights (neurons...
Natural Language Processing (NLP): Teaching Computers to Read
Introduction: The Language Barrier To a computer, the word "Apple" is just a string of bytes (01000001...). It has no concept of fruit, technology, or pie. Natural Language Processing (NLP) is the field of AI focused on enabling computers to understa...
Deep Learning Architectures: CNNs, RNNs, and Transformers
Introduction: Specialized Brains In our last post, we built a standard Neural Network (often called a Dense or Fully Connected network). These are great generalists, but they struggle with specific types of data. Images have spatial structure (pixel...
Neural Networks Explained: From Neurons to Deep Learning
Introduction: Mimicking the Brain Traditional algorithms (like Linear Regression) are great for math, but they struggle with "human" tasks like recognizing a face or understanding a joke. To solve these, scientists looked at the best learning machine...
Unsupervised Learning: Clustering and Dimensionality Reduction Explained
Introduction: Learning Without a Teacher In Supervised Learning, we gave the computer the answer key. But what if we don't have one? What if we just have a massive dump of customer data, satellite images, or genetic sequences, and we have no idea wha...
Supervised Learning Algorithms: A Deep Dive into Regression and Classification
Introduction: The "Teacher" Paradigm Supervised learning = teaching the computer with a teacher. You give it labeled data (inputs + correct answers) and say: "Learn to predict the correct answer for new similar inputs." It's like showing a child 100 ...
Machine Learning Fundamentals: A Beginner-Friendly Guide to AI Concepts
What is Machine Learning? (The "No-Jargon" Explanation) Imagine you want to teach a child to recognize a cat. You wouldn't hand them a rulebook that says: "If it has triangular ears, whiskers, and says meow, it is a cat." That's too rigid. What if th...