Think Different

T he transformer architecture with its key components and examples: Transformer : A deep learning architecture primarily used for natural language processing (NLP) tasks. It's known for its ability to process long sequences of text, capture long-range dependencies, and handle complex language patterns. Key Components: Embedding Layer: Converts input words or tokens into numerical vectors, representing their meaning and relationships. Example: ["I", "love", "NLP"] -> [0. 25, 0. 81, -0. 34], [0. 42, -0. 15, 0. 78], [-0. 12, 0. 54, -0. 68] Encoder : Processes the input sequence and extracts meaningful information. Consists of multiple encoder blocks, each containing: Multi-Head Attention: Allows the model to focus on different parts of the input sequence simultaneously, capturing relationships between words. Feed Forward Network: Adds non-linearity and learns more complex patterns. Layer Normalization: Helps sta...