Inside the Transformer Block: Feedforward Networks and Assembly

To truly understand how modern large language models process language, you must look beyond attention mechanisms and explore how token representations are actually transformed and assembled. This course demystifies the feedforward network and the structural assembly of the Transformer block. You will transition from a high-level user of language models to a practitioner who understands the precise structural steps that occur inside a Transformer layer, preparing you to design, debug, and optimize neural network architectures. What you'll learn: Understand the fundamental role of the feedforward network in processing context vectors; Analyze how residual connections and layer normalization, including modern alternatives like RMSNorm, prevent vanishing gradients; Explore modern activation functions like GeLU and SwiGLU that power state-of-the-art language models; Trace the flow of tokens from input embeddings through attention to final block assembly; Write clean, modular code snippets to construct a complete Transformer block. We start with foundational definitions of vector transformations, then progress step-by-step through the feedforward layer, normalization techniques, and residual pathways, culminating in the complete assembly of a working Transformer block. This course is designed for aspiring AI engineers, software developers, and tech enthusiasts who want a clear, conceptual, and code-based understanding of deep learning architectures. Start reading today to unlock the inner workings of modern artificial intelligence.