Transformers, a game-changer in AI, have revolutionized various domains including natural language processing (NLP), computer vision, and time series prediction. Introduced in 2017, transformers rely on self-attention mechanisms, abandoning traditional recurrent neural networks (RNNs) and convolutional neural networks (CNNs). The encoder processes input data, while the decoder generates output. Self-attention allows models to weigh word importance relative to each other, enabling complex transformations. Positional encodings help maintain sequence information, and layer normalization stabilizes training.

Transformers have set new benchmarks in tasks like text classification, translation, and summarization. They are also used in generative models, such as GPT-3, which can generate human-like text and code. Pre-trained transformer models and libraries are available for developers to fine-tune models on specific tasks.

Source: https://dev.to/asadali00/understanding-transformer-neural-networks-a-game-changer-in-ai-2m3p