Gradient Descent is a popular optimization algorithm used in machine learning. It helps in minimizing a function by iteratively moving towards the minimum. There are two main types of Gradient Descent: Batch Gradient Descent and Stochastic Gradient Descent. Let's dive into the differences between them.
Batch Gradient Descent
Batch Gradient Descent uses the entire training dataset to compute the gradient. This means that for each iteration, it computes the average gradient of the entire dataset.
Pros:
- More accurate as it uses the entire dataset to compute the gradient.
- Converges to the minimum faster.
Cons:
- Computationally expensive as it requires processing the entire dataset.
- Takes longer to train, especially for large datasets.
Stochastic Gradient Descent (SGD)
Stochastic Gradient Descent uses a single randomly selected training example to compute the gradient. This means that for each iteration, it updates the model based on one data point.
Pros:
- Computationally less expensive than Batch Gradient Descent.
- Faster training time, especially for large datasets.
Cons:
- Less accurate as it uses only one data point to compute the gradient.
- Converges slower than Batch Gradient Descent.
Choosing the Right One
The choice between Batch Gradient Descent and Stochastic Gradient Descent depends on the specific problem and the available computational resources.
- For small datasets, Batch Gradient Descent is a good choice.
- For large datasets, Stochastic Gradient Descent is more suitable.
Further Reading
For more information on Gradient Descent and its variants, you can check out our Introduction to Machine Learning guide.