Learning Rate
The learning rate is a hyperparameter that controls the step size during gradient descent optimization. Too high a learning rate causes instability, while too low a rate leads to slow convergence.
Understanding Learning Rate
The learning rate is a critical hyperparameter that controls how much a model's weights are adjusted during each step of gradient descent optimization. A learning rate that is too high causes training to overshoot optimal values and diverge, while one that is too low results in painfully slow convergence or getting stuck in poor local minima. Finding the right learning rate is one of the most impactful aspects of hyperparameter tuning, and techniques like learning rate schedulers, warmup periods, and cyclical learning rates dynamically adjust this value during training. The Adam optimizer and other adaptive methods automatically scale learning rates per parameter, reducing some of the sensitivity. In practice, researchers often use learning rate finder techniques to identify a good starting value. The interaction between learning rate and batch size is also important, with larger batches generally tolerating higher learning rates.
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.