Weight Initialization
Weight initialization is the strategy for setting initial values of neural network weights before training begins. Proper initialization (like Xavier or He initialization) prevents vanishing or exploding gradients.
Understanding Weight Initialization
Weight initialization refers to the strategy used to set the starting values of a neural network's parameters before training begins, and it has a profound impact on training dynamics and final model performance. Poor initialization can lead to vanishing or exploding gradients, causing training to stall or diverge entirely. Foundational methods include Xavier initialization, designed for sigmoid and tanh activations, and He initialization, optimized for ReLU-based networks, both of which calibrate initial weight variance based on layer dimensions. Modern architectures with hundreds of layers, including the deep transformers used in large language models, require careful initialization combined with techniques like gradient clipping and layer normalization to ensure stable training. Weight initialization interacts closely with learning rate selection and batch normalization in determining convergence behavior. Getting this foundational step right is a prerequisite for effective unsupervised pre-training and for the scaling laws that govern how model performance improves with increased parameters.
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.