Description: A minibatch is a small random subset of the training dataset used in each training iteration to update the model. This approach is fundamental in the field of machine learning, as it allows algorithms to learn more efficiently and effectively. By working with minibatches, the goal is to balance model accuracy and training speed. Instead of using the entire dataset, which can be very large and costly in terms of time and computational resources, minibatches enable more frequent and faster updates to the model. This not only accelerates the training process but also helps avoid issues like overfitting, as the model is exposed to different data subsets in each iteration. Additionally, using minibatches facilitates the implementation of optimization techniques such as stochastic gradient descent, which is widely used in training neural networks. In summary, minibatches are a key technique in training machine learning models, providing a balance between efficiency and effectiveness in the learning process.
