Weight
A weight is a numerical parameter in a neural network that determines the strength of the connection between neurons. Weights are learned during training through backpropagation and gradient descent.
Understanding Weight
A weight is a learnable numerical parameter within a neural network that determines the strength of the connection between neurons, directly influencing how input signals are transformed as they flow through the model. During training, weights are iteratively adjusted through backpropagation and gradient descent to minimize the loss function, gradually encoding the patterns and relationships present in the training data. Modern large language models contain billions or even trillions of weights, and the specific configuration of these parameters after training represents the model's accumulated knowledge. Weight initialization strategies, regularization techniques like weight decay, and optimization algorithms all critically affect training dynamics. Techniques such as quantization reduce weight precision from 32-bit to 8-bit or 4-bit representations, enabling efficient model deployment on resource-constrained devices without significant accuracy loss.
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.