<ul><li>Batch Gradient Descent uses the entire dataset to calculate the gradient and update parameters once per epoch. It is accurate but computationally expensive for large datasets.</li><li>Stochastic Gradient Descent updates parameters for each data point, using only one example at a time. It is faster but may result in fluctuations or jagged progress.</li><li>Mini-batch Gradient Descent divides data into small batches and calculates gradients for each batch, balancing accuracy and efficiency. It is the most popular in deep learning.</li><li>The choice of gradient descent algorithm depends on data size, computational resources, and model requirements. Mini-batch Gradient Descent is often preferred for its efficiency and balanced performance.</li></ul>

Understanding Gradient Descent: Types, Usage, and Practical Examples

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