MACHINE LEARNING

Detailed explanation-1: -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.

Detailed explanation-2: -Batch Gradient Descent 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. It also requires the entire training dataset to be in memory and available to the algorithm.

Detailed explanation-3: -Gradient descent is a first-order iterative optimization algorithm for finding a local minimum of a differentiable function. let’s consider a linear model, Y pred= B0+B1(x). In this equation, Y pred represents the output. B0 is the intercept and B1 is the slope whereas x is the input value.