Some advantages of batch gradient descent are its computational efficiency: it produces a stable error gradient and a stable convergence. Some disadvantages are that the stable error gradient can sometimes result in a state of convergence that isn’t the best the model can achieve.

Gradient descent is an optimization algorithm which is commonly-used to train machine learning models and neural networks. Training data helps these models learn over time, and the cost function within gradient descent specifically acts as a barometer, gauging its accuracy with each iteration of parameter updates.

Gradient descent is an optimization algorithm which is commonly-used to train machine learning models and neural networks. Training data helps these models learn over time, and the cost function within gradient descent specifically acts as a barometer, gauging its accuracy with each iteration of parameter updates.