Gradient Clipping
Gradient clipping is a technique that limits the magnitude of gradients during training to prevent exploding gradients. It is essential for stable training of deep networks and recurrent architectures.
Understanding Gradient Clipping
Gradient clipping is a regularization technique used during neural network training to prevent gradients from becoming excessively large, a problem known as the exploding gradient problem. When gradients exceed a predefined threshold, they are scaled down proportionally, ensuring stable parameter updates during backpropagation. This technique is especially critical when training recurrent neural networks and deep transformer architectures where gradient magnitudes can vary dramatically across layers. Gradient clipping is a standard component in the training pipelines of large language models and is often used alongside careful weight initialization and learning rate scheduling. By maintaining controlled gradient flow, it enables more reliable convergence and prevents training from diverging entirely. Practitioners typically choose between clipping by value or clipping by norm, with the latter being more commonly used in modern deep learning frameworks.
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.