From the course: Deep Learning with Python: Optimizing Deep Learning Models

Mini-batch gradient descent - Python Tutorial

From the course: Deep Learning with Python: Optimizing Deep Learning Models

Mini-batch gradient descent

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- [Presenter] Mini-batch gradient descent aims to combine the advantages of both batch gradient descent and stochastic gradient descent by updating the model parameters based on the gradient computed from a small batch of training examples. This batch size is typically larger than one as an SGD, but smaller than the total dataset as in batch gradient descent. Picture this as navigating down the hill using information from a small group of nearby paths. This approach allows for both the speed of SGD and the stability of batch gradient descent. One of the primary benefits of mini-batch gradient descent is its computational efficiency. By processing batches of data, it leverages the power of vectorization and optimized hardware like GPUs and TPUs. This can significantly speed up computations compared to processing single samples as in SGD. The use of many batches allows the algorithm to make efficient use of memory hierarchies and parallel processing capabilities, reducing the time per iteration. Using a batch of samples also helps reduce the variance in the parameter updates. This leads to more stable convergence than what is typically seen with SGD. The updates are less noisy because they're based on an average over multiple samples, which smooths out the random fluctuations. This balance can result in faster overall convergence compared to both batch gradient descent and the stochastic gradient descent algorithm. Mini-batch gradient descent also offers flexibility in choosing the batch size. This allows you to adjust the batch size based on the available computational resources and the specific requirements of your problem. Smaller batches can be used when memory is limited, while larger batches can exploit more computational power when available. This adaptability makes many batch gradient descent a versatile tool in the deep learning toolkit. However, choosing the optimal batch size can be tricky and may require experimentation. If the batch size is too small, the updates may still be noisy and lead to unstable convergence similar to SGD. If it's too large, you may lose the computational benefits and face memory constraints similar to batch gradient descent. Finding the right balance is crucial, and can depend on factors like the complexity of the model, the size of the dataset, and the specifics of the hardware being used. Another limitation is that larger batch sizes require more memory. This can become a problem when dealing with very large datasets or when working with models that have a large number of parameters. In such cases, even mini-batch gradient descent can become resource intensive, potentially necessitating the use of specialized hardware or cloud computing resources. Lastly, mini-batch gradient descent may still converge to a suboptimal solution if the batch size is not appropriately chosen. If the batch size does not adequately capture the diversity of the data, the computed gradients may not accurately reflect the true gradients of the loss function, affecting the convergence and the final model performance. This issue underscores the importance of careful data pre-processing and batch selection strategies.

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