Attention Mechanism
An attention mechanism allows neural networks to focus on the most relevant parts of the input when producing each element of the output. Attention is the foundational innovation behind the Transformer architecture and modern large language models.
Understanding Attention Mechanism
The attention mechanism revolutionized deep learning by enabling neural networks to dynamically focus on the most relevant portions of input data when generating each element of output, rather than trying to compress an entire input into a fixed-size representation. Originally developed to improve machine translation, attention allows a model translating a sentence to look back at specific source words when producing each target word. The self-attention variant, central to the transformer architecture, computes relationships between all positions in a sequence simultaneously, enabling models like BERT and GPT to capture long-range dependencies in text. Multi-head attention extends this by running several attention computations in parallel, each learning different relational patterns. Beyond natural language processing, attention mechanisms have been adapted for computer vision (Vision Transformers), speech recognition, and protein structure prediction. The mechanism's ability to provide interpretable attention weights also contributes to model explainability.
Category
Deep Learning
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Activation Function
An activation function is a mathematical function applied to a neuron's output to introduce non-linearity into a neural network. Common activation functions include ReLU, sigmoid, and tanh, each with different properties for gradient flow.
Adam Optimizer
Adam (Adaptive Moment Estimation) is an optimization algorithm that combines the benefits of AdaGrad and RMSProp. It adapts learning rates for each parameter using estimates of first and second moments of gradients.
Adapter Layers
Adapter layers are small trainable modules inserted into a pre-trained model to enable parameter-efficient fine-tuning. They allow task adaptation while keeping the original model weights frozen.
Autoencoder
An autoencoder is a neural network trained to compress input data into a compact representation and then reconstruct it. Autoencoders are used for dimensionality reduction, denoising, and learning latent representations.
Backpropagation
Backpropagation is the algorithm used to train neural networks by computing gradients of the loss function with respect to each weight. It propagates error signals backward through the network to update weights and minimize prediction errors.
Batch Normalization
Batch normalization is a technique that normalizes layer inputs across mini-batches during training to stabilize and accelerate neural network training. It reduces internal covariate shift and allows higher learning rates.
Batch Size
Batch size is the number of training examples used in one iteration of gradient descent. Larger batches provide more stable gradient estimates but require more memory, while smaller batches add beneficial noise.
Boltzmann Machine
A Boltzmann machine is a stochastic recurrent neural network that can learn a probability distribution over its inputs. Restricted Boltzmann Machines (RBMs) were influential in the deep learning revolution as building blocks for deep belief networks.