While this batching provides computation efficiency, it can still have a long processing time for large training datasets as it still needs to store all of the data into memory. This process referred to as a training epoch. Batch gradient descentīatch gradient descent sums the error for each point in a training set, updating the model only after all training examples have been evaluated. There are three types of gradient descent learning algorithms: batch gradient descent, stochastic gradient descent and mini-batch gradient descent. It’s worth noting that a loss function refers to the error of one training example, while a cost function calculates the average error across an entire training set. Additionally, while the terms, cost function and loss function, are considered synonymous, there is a slight difference between them. At this point, the model will stop learning. It continuously iterates, moving along the direction of steepest descent (or the negative gradient) until the cost function is close to or at zero. This improves the machine learning model's efficacy by providing feedback to the model so that it can adjust the parameters to minimize the error and find the local or global minimum.
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