Self-Attention
Self-attention is an attention mechanism where each element in a sequence computes attention scores with every other element in the same sequence. It enables Transformers to capture long-range dependencies regardless of distance.
Understanding Self-Attention
Self-attention is the mechanism that allows each element in a sequence to compute relevance scores with every other element, enabling a model to capture long-range dependencies regardless of distance. It forms the backbone of the transformer architecture, replacing the sequential processing of recurrent neural networks with highly parallelizable computations. In a self-attention layer, each token generates query, key, and value vectors, and attention weights are computed through scaled dot-product operations. Multi-head attention extends this by running several attention computations in parallel, capturing different types of relationships simultaneously. Self-attention powers models like BERT and GPT, enabling breakthroughs in text generation, machine translation, and even computer vision through vision transformers.
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.