An Overview of Gradient Descent Optimization Algorithms
This comprehensive blog post dives deep into gradient descent optimization algorithms, the preferred method for optimizing neural networks and many machine learning algorithms. It begins by exploring the variants of gradient descent (batch, stochastic, mini-batch), then addresses training challenges like learning rate selection and saddle point problems. The post meticulously details popular gradient-based optimization algorithms including Momentum, Nesterov Accelerated Gradient, Adagrad, Adadelta, RMSprop, Adam, AdaMax, Nadam, and AMSGrad, explaining their mechanisms and update rules. Furthermore, it covers algorithms and architectures for optimizing gradient descent in parallel and distributed settings, along with additional strategies to enhance SGD performance, such as shuffling, curriculum learning, batch normalization, early stopping, and gradient noise.