Multi-Head Attention
Multi-head attention is a mechanism that runs multiple attention operations in parallel, allowing the model to attend to different aspects of the input simultaneously. It is a core component of the Transformer architecture.
Understanding Multi-Head Attention
Multi-head attention is the mechanism at the core of the transformer architecture that allows a model to simultaneously attend to information from different representation subspaces at different positions in the input sequence. Instead of computing a single attention function, multi-head attention runs several attention heads in parallel, each learning to focus on different types of relationships, such as syntactic dependencies, semantic associations, or positional patterns. The outputs from all heads are concatenated and linearly transformed to produce the final result. This design gives transformers their remarkable ability to capture diverse, complex patterns in data, which is fundamental to the success of large language models like GPT and BERT. Multi-head attention scales well and enables efficient parallelization on GPU hardware, contributing to the transformer's dominance over earlier sequential architectures like LSTM in both natural language processing and computer vision.
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.
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.
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.