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
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
🔶 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
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: 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: Understanding Word Order in AI Introduction Transformers have significantly advanced Natural Language Processing (NLP) and Artificial Intelligence (AI). Unlike Recurrent Neural … Read more
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
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
Unit 1: Fundamentals of Artificial Intelligence 1.1 Definition, history, motivation, and need for Artificial Intelligence (AI) 1.2 Goals and contributions of AI to modern technology … Read more
Introduction Bayes’ Theorem is a cornerstone of probabilistic reasoning, serving as a fundamental rule for inference. It describes the probability of an event, based on … Read more
Introduction Uncertainty in AI Probability Basics Probabilistic Inference Applications of Probabilistic Reasoning Conclusion References: Note: This content was generated with the assistance of Google’s Gemini … Read more