A transformer model is a deep learning architecture introduced in the 2017 paper "Attention Is All You Need" by Vaswani et al. It revolutionized natural language processing (NLP) and has since become the backbone of powerful generative AI models such as GPT, BERT, and T5.
Unlike traditional sequential models like RNNs or LSTMs, which process input data step-by-step, transformers handle entire sequences at once using a mechanism called self-attention. This mechanism allows the model to weigh the importance of different words in a sentence relative to each other, regardless of their position. For example, in the sentence "The cat sat on the mat," the word "cat" can be directly related to "sat" without needing to go through each intervening word step-by-step.
The transformer consists of two main parts: the encoder and the decoder. The encoder processes the input data and generates a contextual representation, while the decoder uses this representation to generate the output. Each component is composed of multiple layers that include self-attention, feed-forward networks, and layer normalization, with residual connections to aid training.
Transformers are highly parallelizable, which makes training faster and more efficient on modern hardware like GPUs and TPUs. They are also flexible, being applicable not only to text but also to images, audio, and multimodal data.
Their ability to capture complex patterns and long-range dependencies has enabled groundbreaking applications like machine translation, summarization, text generation, code generation, and image captioning.
In summary, the transformer model is a foundational architecture in deep learning, particularly suited for generative tasks, and plays a central role in many state-of-the-art AI systems. Understanding transformers is essential for anyone pursuing an Applied Generative AI Course.
