👉 Transformer engineering is a revolutionary approach in the field of deep learning, primarily used for natural language processing (NLP) tasks. Unlike traditional recurrent neural networks (RNNs), transformers do not process input data sequentially but instead use a mechanism called self-attention to weigh the importance of different parts of the input relative to each other. This allows transformers to capture complex dependencies and relationships within sequences, such as sentences or paragraphs, more effectively. The core components of transformer engineering include multi-head self-attention, positional encoding to maintain the order of input sequences, and feed-forward neural networks for processing each position independently. These elements work together to enable parallelization during training, significantly speeding up the learning process and improving model performance on tasks like machine translation, text summarization, and question answering.
