Deep Learning

Modern large language models (LLMs) like GPT-4, Llama, and Claude look magical—but they are built on concepts that have matured over decades: neural networks, gradient descent, and clever architectural choices. This tutorial walks you step by step from classic neural networks all the way to LLMs. You’ll see how each idea builds on the previous one, and you’ll get practical code examples along the way. Table of Contents Foundations: What Is a Neural Network? 1.1 The Perceptron 1.2 From Perceptron to Multi-Layer Networks 1.3 Activation Functions ...

In 2017, a team at Google published a paper that would fundamentally reshape the landscape of machine learning. “Attention Is All You Need” by Vaswani et al. introduced the Transformer architecture—a bold departure from the recurrent and convolutional approaches that had dominated sequence modeling for years. The paper’s central thesis was radical: you don’t need recurrence or convolution at all. Just attention mechanisms and feed-forward networks are sufficient to achieve state-of-the-art results in sequence-to-sequence tasks. ...