Transformer architecture, introduced in 2017 by Vaswani et al., revolutionized deep learning by replacing recurrent and convolutional layers with an attention-based mechanism that processes sequences in parallel. Unlike RNNs and LSTMs, transformers eliminate sequential dependencies through self-attention, allowing every token to attend to every other token simultaneously — enabling massively parallel training that scales to billions of parameters. At its core, the transformer relies on three key concepts: multi-head attention for capturing diverse relationships, positional encodings for sequence order awareness, and residual connections with layer normalization for stable deep network training. Understanding transformers is essential not only because they power modern large language models like GPT, BERT, and T5, but also because their architectural innovations — from encoder-decoder structures to efficient attention variants — have influenced nearly every domain in AI, from vision (ViT) to multimodal systems.