Pruning
Pruning is a model compression technique that removes unnecessary weights or neurons from a neural network to reduce its size and computational cost. Pruned models can be significantly smaller while maintaining most of their accuracy.
Understanding Pruning
Pruning is a model compression technique that removes redundant or low-importance parameters from a neural network to reduce its size, computational cost, and inference latency while maintaining acceptable performance. The approach is based on the observation that many trained networks contain significant redundancy, with numerous weights contributing minimally to predictions. Structured pruning removes entire neurons, filters, or attention heads, while unstructured pruning zeroes out individual weights based on magnitude or sensitivity analysis. Pruning is especially valuable for deploying deep learning models on edge devices like smartphones and IoT sensors where memory and processing power are limited. The lottery ticket hypothesis, a landmark finding in deep learning research, suggests that dense networks contain sparse subnetworks that can match the full model's performance when trained in isolation. Pruning is often combined with quantization and knowledge distillation for maximum compression.
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.