explain transformers in ML and how it works on a high level

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Transformers are a revolutionary type of neural network architecture that transformed the field of Natural Language Processing. Unlike traditional sequential models, Transformers use attention mechanisms to process entire sequences simultaneously, making them highly efficient and effective for tasks like translation, text generation, and language understanding. The first step in Transformer processing is input preparation. Each word in the input sequence is converted into a numerical vector called an embedding. These embeddings capture semantic meaning but lose positional information. To solve this, positional encodings are added to preserve the order of words. This combination creates rich representations that maintain both meaning and position, enabling the Transformer to process the entire sequence in parallel. The attention mechanism is the core innovation of Transformers. It allows each word to dynamically focus on relevant parts of the input sequence. The mechanism uses three components: Query, Key, and Value matrices. For each word, we compute attention weights by comparing its query with all keys, then apply softmax to get probabilities. These weights determine how much each word should attend to others. In this example, when processing 'cat', the model pays most attention to itself and related words, creating context-aware representations. The Transformer architecture consists of two main components: the Encoder and Decoder stacks. The Encoder processes the entire input sequence simultaneously using self-attention, where each word can attend to all other words in the input. The Decoder generates the output sequence one word at a time, using masked self-attention to prevent looking at future words, and cross-attention to focus on relevant parts of the encoded input. This architecture enables efficient parallel processing while maintaining the sequential nature of language generation. Transformers have revolutionized artificial intelligence across multiple domains. Starting with machine translation in 2017, they quickly expanded to power breakthrough models like BERT for language understanding and GPT for text generation. Today, Transformers enable applications from code generation to image processing with Vision Transformers. Their key advantages include efficient parallel processing, ability to capture long-range dependencies, and excellent transfer learning capabilities. This architecture has become the foundation for modern AI systems, enabling the current generation of large language models and multimodal AI applications.