What Is Vector Representation? How Transformers Understand Text
In the context of input embedding in a Transformer, a vector representation means that each word (or subword/token) from the input sequence is mapped to … Read more
LLM
Self Attention in Transformer
🔶 What is Self-Attention? Self-attention is the core mechanism in the Transformer architecture (Vaswani et al., 2017) that allows the model to weigh the importance … Read more
Why 512 Dimensions in Transformer Model Architecture
In the original Transformer (Vaswani et al., 2017), each input token is represented as a 512-dimensional vector. This isn’t arbitrary — it’s a design choice … Read more
Multi-Head Attention in Transformers
Multi-Head Attention in Transformers: Understanding Context in AI Introduction The Multi-Head Attention (MHA) mechanism is a fundamental component of the Transformer architecture, playing a crucial … Read more
Positional Encoding in Transformers
Positional Encoding in Transformers: Understanding Word Order in AI Introduction Transformers have significantly advanced Natural Language Processing (NLP) and Artificial Intelligence (AI). Unlike Recurrent Neural … Read more
Input Embedding in Transformers
Understanding Input Embedding in Transformers Introduction When processing natural language, neural networks cannot directly interpret raw text. Instead, words, subwords, or characters must be converted … Read more
Transformer Architecture in LLM
Understanding Transformer Architecture: The Foundation of Large Language Models Introduction The Transformer architecture has revolutionized the field of natural language processing (NLP) and artificial intelligence … Read more