Artificial Intelligence

          Gradient Descent is known as one of the most usually operated optimization algorithms to train machine learning models by measure of minimizing errors between real and anticipated results. Further, gradient descent is also utilized to train Neural Networks.   Gradient descent is one of the most popularized algorithms to carry out optimization and by far the most familiar way to optimize neural networks. At the same time, every state-of-the- craft Deep Learning library contains executions of varied algorithms to optimize gradient descent. These algorithms, still, are frequently applied as black-box optimizers, as practical explanations of their strengths and faults are tough to come by.   Advantages     Easy Calculation.   Easy to Apply     Easy to decide   Disadvantages     May trap at original minima.   Weights are altered after computing the gradient on the whole dataset. so, if the datasets are too large also this may take days to meet to the minima.     Different types of