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Transformer Networks are a type of neural network architecture that relies on self-attention mechanisms to process input data, enabling parallelization and improved performance on tasks like natural language processing. They have revolutionized the field by allowing models to capture long-range dependencies and contextual information more effectively than previous architectures like RNNs and LSTMs.
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Self-attention is a mechanism in neural networks that allows the model to weigh the importance of different words in a sentence relative to each other, enabling it to capture long-range dependencies and contextual relationships. It forms the backbone of Transformer architectures, which have revolutionized natural language processing tasks by allowing for efficient parallelization and improved performance over sequential models.
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Multi-Head Attention is a mechanism that allows a model to focus on different parts of an input sequence simultaneously, enhancing its ability to capture diverse contextual relationships. By employing multiple attention heads, it enables the model to learn multiple representations of the input data, improving performance in tasks like translation and language modeling.
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Positional encoding is a technique used in transformer models to inject information about the order of input tokens, which is crucial since transformers lack inherent sequence awareness. By adding or concatenating Positional encodings to input embeddings, models can effectively capture sequence information without relying on recurrent or convolutional structures.
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Feedforward Neural Networks are a type of artificial neural network where connections between the nodes do not form a cycle, meaning data moves in one direction from input to output. They are the simplest form of neural networks and are primarily used for pattern recognition and classification tasks.
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Layer Normalization is a technique used to stabilize and accelerate the training of deep neural networks by normalizing the inputs across the features for each training case. It differs from Batch Normalization by normalizing the inputs within a single layer, making it more suitable for recurrent neural networks and situations where batch sizes are small or vary.
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Encoder-Decoder Architecture is a neural network design pattern used to transform one sequence into another, often applied in tasks like machine translation and summarization. It consists of an encoder that processes the input data into a context vector and a decoder that generates the output sequence from this vector, allowing for flexible handling of variable-length sequences.
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Attention mechanisms are a crucial component in neural networks that allow models to dynamically focus on different parts of the input data, enhancing performance in tasks like machine translation and image processing. By assigning varying levels of importance to different input elements, Attention mechanisms enable models to handle long-range dependencies and improve interpretability.
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Sequence-to-Sequence models are a class of neural networks designed to transform one sequence into another, often used in tasks like machine translation, summarization, and conversational agents. They typically employ encoder-decoder architectures, where the encoder processes the input sequence into a context vector and the decoder generates the output sequence from this vector, often using techniques like attention to improve performance.
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BERT (Bidirectional Encoder Representations from Transformers) is a groundbreaking natural language processing model developed by Google that uses transformers to achieve state-of-the-art results on a wide range of NLP tasks. By leveraging bidirectional training, BERT captures context from both directions in a text sequence, significantly improving the understanding of word meaning and context compared to previous models.
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GPT, or Generative Pre-trained Transformer, is an advanced language model developed by OpenAI that uses deep learning to produce human-like text. It leverages a transformer architecture to predict the next word in a sentence, enabling it to generate coherent and contextually relevant responses across a wide range of topics.
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Temporal attention is a mechanism in neural networks that dynamically focuses on different parts of a sequence over time, enhancing the model's ability to capture temporal dependencies in sequential data. It is particularly useful in tasks such as video analysis, speech recognition, and time-series forecasting, where understanding the progression and context of information is crucial.
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