Distillation
Knowledge distillation is a model compression technique where a smaller student model learns to replicate the behavior of a larger teacher model. Distillation makes it possible to deploy powerful AI in resource-constrained environments.
Understanding Distillation
Distillation, or knowledge distillation, is a model compression technique where a smaller "student" model is trained to replicate the behavior of a larger, more capable "teacher" model. Rather than learning solely from hard labels, the student learns from the teacher's soft probability distributions, capturing nuanced relationships between classes that the teacher has discovered. This approach is widely used to deploy deep learning models on resource-constrained devices for edge AI applications, such as mobile phones and IoT sensors, where a full-sized foundation model would be impractical. Distillation has been applied to compress large language models like GPT into smaller variants that retain much of the original performance. The technique can be combined with other optimization methods like quantization and pruning to further reduce model size and inference latency.
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.