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Gradient Descent is an optimization algorithm to train neural networks.
The algorithm computes how much we need to adjust the model to get closer to the desired results on every iteration.
Here is how it works in a couple of sentences:
We take samples from the training dataset, run them through the model, and determine how far away our results are from the ones we expect.
We use this "error" to compute how much we need to update the model weights to improve the results.
A critical decision we need to make is how many samples we use on every iteration.
We have three choices:
• Use a single sample of data
• Use all of the data at once
• Use some of the data
Using a single sample of data on every iteration is called "Stochastic Gradient Descent" (SGD.)
The algorithm uses one sample at a time to compute the updates.
Advantages of Stochastic Gradient Descent:
• Simple to understand
• Avoids getting stuck in local minima
• Provides immediate feedback
• Computationally intensive
• May not settle in the global minimum
• The performance will be noisy
Using all the data at once is called "Batch Gradient Descent."
After processing every sample, the algorithm takes the entire dataset and computes the updates.
Advantages of Batch Gradient Descent:
• Computationally efficient
• Stable performance (less noise)
• Requires a lot of memory
• May get stuck in local minima
Using some data (more than one sample but fewer than the entire dataset) is called "Mini-Batch Gradient Descent."
The algorithm works like Batch Gradient Descent, the only difference being that we use fewer samples.
Advantages of Mini-Batch Gradient Descent:
• Avoids getting stuck in local minima
• More computationally efficient than SGD
• Doesn't need as much memory as BGD
• New hyperparameter to worry about
We usually call this hyperparameter "batch_size."
We rarely use Batch Gradient Descent in practice, especially with large datasets.
Stochastic Gradient Descent (using a single sample at a time) is not that popular either.
Mini-Batch is the most used.
There's a lot of research around the optimal batch size.
Your problem differs from any other problem, but empirical evidence suggests that smaller batches perform better.
(Small as in less than a hundred or so.)