Transformer Decoder: Feedforward and Normalization

Unraveling the inner workings of the Transformer decoder is crucial for anyone building or understanding modern AI models. This course demystifies its core components. By the end of this course, you will have a solid foundational understanding of the Transformer decoder's architecture, enabling you to comprehend how it processes input and produces output, a key skill for working with advanced NLP models. What you'll learn: * Understand the fundamental role of the decoder within the Transformer architecture * Learn the structure and function of the Feedforward Neural Network layer * Grasp the purpose and implementation of the Add & Norm (Layer Normalization) components * Explore how Linear layers transform decoder outputs for prediction * Apply the Softmax function to convert raw scores into probability distributions for token generation * Understand the basic principles of sequence generation and how these layers contribute to it * Analyze the interplay between these components in processing sequential data and generating responses This course begins with an introduction to the Transformer decoder's overall purpose, then systematically breaks down each essential layer – the Feedforward Network, Add & Norm, Linear, and Softmax – explaining their individual functions and how they integrate to perform sequence generation. This course is designed for absolute beginners interested in machine learning and natural language processing, with no prior experience in Transformer architecture required. It is ideal for those looking to build a foundational understanding of modern AI models. Start your journey into the heart of Transformer models today.