Batch Gradient Descent

Batch Gradient Descent is an optimization algorithm used in machine learning to minimize a cost function by iteratively adjusting model parameters. It computes the gradient of the cost function with respect to all parameters using the entire training dataset in each iteration, ensuring a stable and consistent update direction. This method is fundamental for training models like linear regression and logistic regression, providing a straightforward approach to finding optimal parameters.

Also known as: Full Gradient Descent, Vanilla Gradient Descent, BGD, Batch GD, Gradient Descent

Why learn Batch Gradient Descent?

Developers should learn Batch Gradient Descent when working on supervised learning tasks where the training dataset is small to moderate in size, as it guarantees convergence to the global minimum for convex functions. It is particularly useful in scenarios requiring precise parameter updates, such as in academic research or when implementing algorithms from scratch to understand underlying mechanics. However, it can be computationally expensive for large datasets, making it less suitable for big data applications compared to variants like Stochastic Gradient Descent.

Learning Resources

• docs: Stanford CS229 Lecture Notes on Gradient Descent
• course: Coursera Machine Learning Course by Andrew Ng
• tutorial: Gradient Descent Explained Visually
• tutorial: Batch Gradient Descent Implementation in Python
• book: Deep Learning Book by Ian Goodfellow et al.