This blog post provides a comprehensive guide to the Transformer neural network architecture, which was introduced in the 2017 paper “Attention is All You Need”. The Transformer model, initially designed for neural machine translation, has proven to be a versatile tool for various applications beyond Natural Language Processing (NLP). The post delves into the key components of the Transformer model, including its attention mechanism, encoder-decoder structure, and the use of large language models. It also discusses the applications of Transformer models beyond NLP, current challenges, and potential future directions. The post further explores the concept of attention, multi-head attention, and the role of Multi-Layer Perceptrons (MLPs) in the Transformer architecture. It also explains the importance of embeddings and positional encoding layers, residual connections, layer normalization, and dropout in the Transformer model. The post concludes by discussing the visualization of attention in the Transformer model.