The Decoder Unveiled: Complete Guide to Transformer Output Processing Architecture

Understanding decoder layers is fundamental for building effective transformer models used in machine translation and language generation. This article details the essential mechanisms—positional encoding, self-attention, and residual connections—that enable decoders to maintain context and produce coherent output. Mastering these architectural components is critical as transformer-based systems continue to dominate modern NLP applications.