## Is Adadelta and RMSProp same?

Adadelta The difference between Adadelta and RMSprop is that Adadelta removes the use of the learning rate parameter completely by replacing it with D, the exponential moving average of squared deltas.

## What is Adadelta?

Adadelta is a more robust extension of Adagrad that adapts learning rates based on a moving window of gradient updates, instead of accumulating all past gradients. This way, Adadelta continues learning even when many updates have been done.

**Which is the best optimizer?**

Adam is the best optimizers. If one wants to train the neural network in less time and more efficiently than Adam is the optimizer. For sparse data use the optimizers with dynamic learning rate. If, want to use gradient descent algorithm than min-batch gradient descent is the best option.

**Is stochastic gradient descent faster?**

Stochastic gradient descent (SGD or “on-line”) typically reaches convergence much faster than batch (or “standard”) gradient descent since it updates weight more frequently.

### Is Adam better than SGD?

By analysis, we find that compared with ADAM, SGD is more locally unstable and is more likely to converge to the minima at the flat or asymmetric basins/valleys which often have better generalization performance over other type minima. So our results can explain the better generalization performance of SGD over ADAM.

### Why Adam Optimizer is best?

The results of the Adam optimizer are generally better than every other optimization algorithms, have faster computation time, and require fewer parameters for tuning. Because of all that, Adam is recommended as the default optimizer for most of the applications.

**Is Adadelta adaptive?**

An Adaptive Learning Rate Algorithm (AdaDelta) is a Gradient Descent-based Learning Algorithm that uses the exponential decay rate of the first- and second-order moments.

**Is SGD with momentum better than Adam?**

SGD is better? One interesting and dominant argument about optimizers is that SGD better generalizes than Adam. These papers argue that although Adam converges faster, SGD generalizes better than Adam and thus results in improved final performance.

## Is Adam better than Adadelta?

Insofar, RMSprop, Adadelta, and Adam are very similar algorithms that do well in similar circumstances. Kingma et al. show that its bias-correction helps Adam slightly outperform RMSprop towards the end of optimization as gradients become sparser. Insofar, Adam might be the best overall choice.

## What is the disadvantage of Stochastic Gradient Descent SGD )?

Due to frequent updates, the steps taken towards the minima are very noisy. This can often lean the gradient descent into other directions. Also, due to noisy steps, it may take longer to achieve convergence to the minima of the loss function.

**Why is SGD stochastic?**

Stochastic Gradient Descent (SGD): The word ‘stochastic’ means a system or a process that is linked with a random probability. Hence, in Stochastic Gradient Descent, a few samples are selected randomly instead of the whole data set for each iteration.