Stochastic Gradient Descent
Stochastic Gradient Descent (SGD) is an optimization algorithm that updates model weights using the gradient computed from a random subset (mini-batch) of training data. SGD is computationally efficient and adds beneficial noise that helps escape local minima.
Understanding Stochastic Gradient Descent
Stochastic Gradient Descent (SGD) is an optimization algorithm that updates model parameters using the gradient computed from a single randomly selected training example or a small mini-batch, rather than the entire dataset. This stochastic approach introduces noise that can help the model escape local minima and often leads to better generalization compared to full-batch gradient descent. SGD is the foundational optimizer behind virtually all deep learning training, though modern variants like the Adam optimizer, RMSProp, and AdaGrad add adaptive learning rates for faster convergence. Key hyperparameters include the learning rate, momentum, and batch size, each significantly affecting training dynamics. Despite its simplicity, SGD with proper tuning remains competitive with more sophisticated optimizers across many training scenarios.
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.