Backpropagation and Gradient Descent: The Engines of Deep Learning Table of Contents 1. Introduction Every neural network learns by minimizing a loss function — a measure of how wrong its predictions are. But how does the network know which direction to adjust its thousands (or billions) of weights? Two algorithms answer this question: Without backpropagation, training deep networks would…
Modern AI systems no longer rely solely on keyword matching to retrieve information. Instead, they leverage semantic understanding to identify content that is contextually relevant, even when exact words do not match. Imagine searching for: A traditional keyword-based search engine may fail to retrieve a document titled: because the keywords are different. Humans immediately recognize…
Introduction You understand self-attention, multi-head attention, encoder-decoder, and positional encoding. Now the question: How do you actually use Transformers in production? This post covers: Real-World Applications of Transformer Architecture 1. Machine Translation (The Original Use Case) Example: Google Translate, DeepL How it uses Transformers: 2. Text Summarization Example: ChatGPT summarizing long documents, automated news digests Architecture: Encoder-decoder (T5,…