Adam Optimizer
Optimizers update weights with the help of loss functions. Selecting the wrong optimizer or the wrong hyperparameter for the optimizer can lead to a delay in finding the optimal solution for the problem.
The name Adam is derived from adaptive moment estimation. Adam has been designed specifically for training deep neural networks. The use of Adam is widespread in the data science community due to its speed in getting close to the optimal solution. Thus, if you want fast convergence, use the Adam optimizer. Adam does not always lead to the optimal solution; in such cases, SGD with momentum helps achieve state-of-the-art results. The following would be the parameters:
- Learning rate: This is the step size for the optimizer. Larger values (0.2) result in faster initial learning, whereas smaller values (0.00001) slow the learning down during training.
- Beta 1: This is the exponential decay rate for the mean estimates of the gradient.
- Beta 2: This is the exponential decay...
