Final answer:
Batch gradient descent is a kind of Gradient Descent (GD) that updates the model's parameters using the entire batch of training examples in each iteration. It offers better convergence but can be computationally expensive and memory-intensive.
Step-by-step explanation:
The kind of Gradient Descent (GD) called batch gradient descent is referred to as such because it updates the model's parameters using the entire batch of training examples in each iteration. In other words, the gradient is computed by summing the gradients of all the training examples. This is in contrast to other variations of GD, such as stochastic gradient descent, which updates the model's parameters using one training example at a time.
Batch gradient descent is commonly used when there is sufficient computational resources available to process the entire batch of training examples simultaneously. It offers the advantage of better convergence to the optimal solution, as the gradient is computed using more information from the entire dataset.
However, batch gradient descent can be computationally expensive and memory-intensive, especially for large datasets, because it requires storing all the training examples in memory to compute the gradient. This can limit its practicality in certain scenarios.