In the context of machine learning, a mini-batch refers to a subset of a larger dataset used in each iteration or epoch during the training process of a model, particularly in gradient-based optimization algorithms. Instead of processing the entire dataset at once (batch training) or using individual data points (stochastic training), a mini-batch provides a balance between computational efficiency and convergence stability. It enables faster computation by leveraging parallel processing capabilities while allowing for a more stable estimation of the gradient compared to stochastic gradient descent. The size of the mini-batch is a hyperparameter to be tuned and is a critical factor in the efficiency and performance of the training process.
Mini-batch meaning with examples
- During training, a neural network utilized a mini-batch size of 32. This means that in each iteration, the model processed 32 data points at once to compute the gradients, update the weights, and improve its predictions. This strategy significantly accelerated the training process, resulting in faster convergence and improved model performance.
- To optimize a complex image recognition model, the engineers chose a mini-batch size of 128. This approach helped smooth out the stochasticity of the gradient updates, providing a more stable and predictable training path. They experimented with different sizes to determine the optimal trade-off between speed and accuracy.
- A researcher applied mini-batch gradient descent to train a recurrent neural network (RNN) on a large text corpus. By processing mini-batches of sequences, the model was able to learn long-range dependencies in the text efficiently and avoided the computational expense of processing the entire corpus in a single batch.
- In a deep learning project dealing with tabular data, the team implemented a mini-batch size of 64 for their model training. This strategy allowed them to leverage the parallelism of their hardware more effectively, speeding up the training time and enabling quicker experimentation with different model architectures and hyperparameters.