Why Mini Batch Size Is Better Than One Single “Batch” With All Training Data?

Answer: Mini batch size is better than one single batch with all training data because it allows for more efficient and effective optimization during training.

Mini batch size refers to splitting the training data into smaller subsets or batches during the training process, whereas using a single batch with all the training data is known as batch gradient descent. Mini batch size offers several advantages over using a single batch:

1. Efficient Memory Usage: Mini batches allow for better memory utilization, particularly when working with large datasets that may not fit into memory all at once. By processing data in smaller chunks, it becomes feasible to train models on computers with limited memory resources.
2. Faster Convergence: Mini batch gradient descent often converges faster compared to batch gradient descent. The reason behind this is that updating the model’s parameters with each mini batch allows for more frequent adjustments, leading to quicker convergence towards the optimal solution.
3. Improved Generalization: Mini batch gradient descent tends to offer better generalization performance. This is because each mini batch provides a noisy estimate of the gradient, which helps the optimization process escape local minima and saddle points more effectively, leading to a more robust and generalizable model.
4. Enhanced Parallelism: Mini batches can be processed concurrently, enabling parallel computation and speeding up the training process, especially when using hardware accelerators like GPUs or TPUs.
5. Stochasticity: The use of mini batches introduces stochasticity into the optimization process, which can help the model avoid getting stuck in local minima and explore the solution space more effectively.

Conclusion:

In conclusion, employing mini batch size during training offers various benefits such as efficient memory usage, faster convergence, improved generalization, enhanced parallelism, and stochasticity, making it a preferred choice over using a single batch with all the training data.