The Growing Challenge of Model Size Imagine building a house and ordering 10,000 bricks, but you only use 2,000 of them. The remaining 8,000 bricks sit in your yard, taking up space, costing money...

Large Language Models (LLMs) like GPT-4, Claude, and Llama don’t just generate text—they offer precise control over how that text is generated. Understanding inference parameters is like learning t...

Welcome to the final part of our Transformers series! In parts 1 through 5, we’ve built the complete architecture. Now let’s understand how to train it and where it’s used. Training vs Inference ...

In Part 4, we completed our understanding of the encoder. Now let’s explore the decoder—the component that generates output sequences. The Decoder: Overview The decoder is similar to the en...

In Part 3, we explored the multi-head attention mechanism. Now let’s understand the other crucial components that complete the encoder layer. Recap: The Encoder Layer Each encoder layer has two m...

In Part 2, we learned how Transformers convert text into meaningful embeddings with positional information. Now we’ll explore the heart of the Transformer: the attention mechanism. The Core Innova...

In Part 1, we explored RNNs and understood why we need attention. Now, let’s dive into the Transformer architecture and understand how it prepares input data for processing. Transformer Architectu...

Welcome to this comprehensive series on understanding Transformers from the ground up! In this first part, we’ll explore the evolution from Recurrent Neural Networks (RNNs) to the attention mechani...

Introduction Principal Component Analysis (PCA) is a widely used dimensionality reduction technique in machine learning. It helps in transforming high-dimensional data into a lower-dimensional spac...

Practical optimization techniques, roofline model, and tools for measuring GPU memory performance