Attention Mechanisms

Information Retrieval: The process of retrieving relevant information from a large collection of data, such as documents or web pages.

Neural Networks: A system of algorithms modeled on the human brain that can recognize patterns and make predictions based on data.

Deep Learning: A branch of machine learning that uses neural networks with many layers to improve accuracy and performance.

Language Models: A statistical model that predicts the probability of a sequence of words in natural language.

Word Embeddings: A technique for representing words as vectors in a high-dimensional space based on their context and meaning.

Recurrent Neural Networks (RNNs): A type of neural network that can process sequential data such as natural language text.

Long Short-Term Memory (LSTM): A type of RNN that can remember previous inputs and selectively forget irrelevant information.

Transformer architecture: A type of neural network architecture that can be used to process sequence data with attention mechanisms.

Attention Mechanisms: A neural network component that allows the model to focus on specific parts of the input during processing.

Seq2seq Models: A type of neural network used for sequence-to-sequence generation, especially in natural language processing tasks such as machine translation.

Encoder-Decoder models: A type of neural network architecture that has an encoder network to represent input data and a decoder network to generate outputs.

Masked Language Modeling (MLM): A task in which a model must predict missing words in a sentence using surrounding context.

Named Entity Recognition (NER): The process of identifying and classifying entities such as people, locations, and organizations in text.

Language Generation: The process of generating text that is fluent, grammatically correct, and coherent.

Machine Translation: The process of translating text from one language to another using machine learning models.

Bahdanau Attention: This attention mechanism is a type of attention mechanism that is commonly used in neural machine translation. It provides a weighted and context-aware representation of the input sequence.

Transformer Self-Attention: This is a type of attention mechanism that is used in transformer-based models, which have become the state-of-the-art architecture for many natural language processing tasks. Transformer self-attention allows the model to be aware of all the words in the sequence, which helps it to better understand the context.

Key-Value Attention: Key-value attention is a type of attention mechanism that is commonly used in neural machine translation. It works by using the keys and values of the input sequence to generate a weighted representation.

Convolutional Self-Attention: This attention mechanism is used in convolutional neural networks (CNNs) for natural language processing tasks. It allows the model to identify important features or patterns in the input sequence by applying attention to different parts of the sequence.

Scaled Dot-Product Attention: Scaled dot-product attention is a type of attention mechanism that is commonly used in transformer-based models. It allows the model to calculate the importance of each word in the input sequence by calculating the dot product of the corresponding word vectors.

Soft Attention: Soft attention is a type of attention mechanism that is commonly used in neural machine translation. It works by providing a weighted representation of the input sequence, which is then fed into the decoder to generate the target sequence.

Local Attention: Local attention is a type of attention mechanism that focuses only on a small subset of the input sequence, instead of the entire sequence. This can be useful when dealing with long sequences, as it reduces the computational complexity of the model.

Multi-Head Attention: Multi-head attention is a type of attention mechanism that is used in transformer-based models. It allows the model to attend to different parts of the input sequence simultaneously, which can improve its ability to capture complex patterns and relationships in the data.

Hard Attention: Hard attention is a type of attention mechanism that is used in some neural network architectures for natural language processing tasks. It works by selecting only a subset of the input sequence to pay attention to, rather than providing a weighted representation of the entire sequence.