How Does AI Read Text? | Attention Is All You Need — Part 2

Before we get into the nitty-gritty details of the Transformer, let's first talk about **tokenization**. A tokenizer converts text into a sequence of *tokens* that a Transformer can work with. Every Transformer-based model has an associated tokenizer and a *vocabulary* — the set of tokens that the model can represent. These tokens are then converted into numerical vectors using the **embedding layer**, which we'll cover in the next video. One of the popular approaches used to build a tokenizer's vocabulary is **Byte Pair Encoding (BPE)**. In this video, we'll understand: • Why do Transformers need tokenization? • What exactly is a token and a vocabulary? • How can we visualize tokenization? • How does *Byte Pair Encoding (BPE)* work? • How can we implement BPE step by step using Python in a Google Colab notebook? I'll also share an example from my own research on *HindiLLM**, where I compared the standard **GPT-2 tokenizer* with a custom tokenizer designed specifically for Hindi text. The result was interesting: the HindiLLM tokenizer was **more than 2× more efficient for Hindi**, representing the same text using almost **half the number of tokens**. And this shows why tokenization matters. *A tokenizer is not just a simple preprocessing step. It can have a significant impact on how efficiently a model represents and processes a language.* 🔗 Tokenizer Visualization Tool: https://tiktokenizer.vercel.app/ 📄 Paper: Attention Is All You Need — Vaswani et al., 2017 📚 *Transformer Series* Part 1 — Transformer Architecture *Part 2 — Tokenization & BPE* Part 3 — Embeddings & Positional Encoding Part 4 — Self-Attention with Calculations Part 5 — Multi-Head Attention ...and more Transformer Playlist:    • Transformer Explained | Attention Is All Y...   Subscribe to follow the complete series and learn **Transformers from scratch**. #Tokenization #BPE #BytePairEncoding #Transformer #AttentionIsAllYouNeed #LLM #NLP #DeepLearning #GenerativeAI #MachineLearning