Gradient Descent Gradient Descent is sensitive to the choice of learning rate alpha. It’s also slow for large data sets. There are, however, improvements to this algorithm. One of them is the minibatch stochastic gradient descent. This speeds up the computation by approximating the gradient using smaller subsets of the training data. Stochastic gradient descent has different versions. One is adagrad which scales alpha for each parameter according to the history of gradients. Another is momentum which accelerates stochastic gradient descent by orienting the gradient descent in the relevant direction and reducing oscillations. The most frequently used is RMSprop and Adam.
A General Guide to Machine Learning (Part Two)
A General Guide to Machine Learning (Part…
A General Guide to Machine Learning (Part Two)
Gradient Descent Gradient Descent is sensitive to the choice of learning rate alpha. It’s also slow for large data sets. There are, however, improvements to this algorithm. One of them is the minibatch stochastic gradient descent. This speeds up the computation by approximating the gradient using smaller subsets of the training data. Stochastic gradient descent has different versions. One is adagrad which scales alpha for each parameter according to the history of gradients. Another is momentum which accelerates stochastic gradient descent by orienting the gradient descent in the relevant direction and reducing oscillations. The most frequently used is RMSprop and Adam.