A Visual Guide to Stochastic, Mini-batch, and Batch Gradient Descent

...with advantages and disadvantages.

Gradient descent is a widely used optimization algorithm for training machine learning models.

Stochastic, mini-batch, and batch gradient descent are three different variations of gradient descent, and they are distinguished by the number of data points used to update the model weights at each iteration.

🔷 Stochastic gradient descent: Update network weights using one data point at a time.

• Advantages:

  • Easier to fit in memory.

  • Can converge faster on large datasets and can help avoid local minima due to oscillations.

• Disadvantages:

  • Noisy steps can lead to slower convergence and require more tuning of hyperparameters.

  • Computationally expensive due to frequent updates.

  • Loses the advantage of vectorized operations.

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🔷 Mini-batch gradient descent: Update network weights using a few data points at a time.

• Advantages:

  • More computationally efficient than batch gradient descent due to vectorization benefits.

  • Less noisy updates than stochastic gradient descent.

• Disadvantages:

  • Requires tuning of batch size.

  • May not converge to a global minimum if the batch size is not well-tuned.

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🔷 Batch gradient descent: Update network weights using the entire data at once.

• Advantages:

  • Less noisy steps taken towards global minima.

  • Can benefit from vectorization.

  • Produces a more stable convergence.

• Disadvantages:

  • Enforces memory constraints for large datasets.

  • Computationally slow as many gradients are computed, and all weights are updated at once.

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Over to you: What are some other advantages/disadvantages you can think of? Let me know :)

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Find the code for my tips here: GitHub.

I like to explore, experiment and write about data science concepts and tools. You can read my articles on Medium. Also, you can connect with me on LinkedIn and Twitter.