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.
Understanding Batch Size
Batch size is a critical hyperparameter that determines how many training examples a neural network processes before updating its weights through backpropagation, directly influencing training speed, memory usage, and model generalization. A batch size of one (stochastic gradient descent) provides noisy but frequent updates that can help escape local minima, while using the full dataset gives precise but computationally expensive gradient estimates. In practice, mini-batch sizes between 32 and 512 are common, offering a balance between gradient accuracy and training efficiency. Larger batch sizes enable better GPU utilization through parallelism but can lead to sharper minima that generalize poorly. The relationship between batch size and the learning rate used with optimizers like the Adam optimizer is an important consideration during hyperparameter tuning. Memory constraints often set an upper limit on batch size, particularly when training large language models with batch normalization layers.
Category
Deep Learning
Is AI recommending your brand?
Find out if ChatGPT, Perplexity, and Gemini mention you when people search your industry.
Check your brand — $9Related Deep Learning Terms
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.
Boltzmann Machine
A Boltzmann machine is a stochastic recurrent neural network that can learn a probability distribution over its inputs. Restricted Boltzmann Machines (RBMs) were influential in the deep learning revolution as building blocks for deep belief networks.