In the previous definition of exponentially weighted average, when t is small, we only consider a few data points which may cause bias. Biased Correction is thus to help correct it.
$$v_t'=\frac{v_t}{1-\beta^t}$$

For example, in the following diagram bias has happened because in early days the estimated temperature is influenced by $v_0 = 0$. We are looking for the green function, but we have estimated the purple one that indicates biased estimates on early days.

University of Michigan - Ann Arbor

Exponentially weighted average is a technique frequently used for time-series data. By taking the average sum of previous data, you could smooth your data series and get an approximate trend of it. 

Consider you have a series of data points $\theta_0,...,\theta_n$,
$$\left\{ \begin{array}{ll}v_t = \theta_t & t=0 \\
v_t = \beta v_{t-1} +(1-\beta)\theta_t & otherwise \end{array}\right.$$
If we expand the second formula,
$v_t  = \beta v_{t-1}+(1-\beta)\theta_t$ 
      $= (1-\beta)\theta_t+\beta(\beta v_{t-2}+(1-\beta)\theta_{t-1})$
      $= (1-\beta)\theta_t + (1-\beta)\beta\theta_{t-1}+ (1-\beta)\beta^2\theta_{t-2}+...$
To get a sense of how the weighted term changes as $\beta$ gets closer to 1,
$$(1 - \epsilon)^{1 / \epsilon}\approx \frac{1}{\epsilon} \Rightarrow \beta^{1/(1-\beta)}\approx \frac{1}{\epsilon}$$
If we denote $w_i$ be the weight we assign to $\theta_i$, then
                                         $w_{t-1/(1-\beta)}=\frac{1}{\epsilon}w_t$
Therefore, we are approximately average over $1/(1-\beta)$ days when calculating $v_t$.

Exponentially Weighted Average

https://www.coursera.org/learn/deep-neural-network?specialization=deep-learning

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

Assume we have a data series of temperature (blue dots), we could use the formula shown in the parent node to get an approximate trend. 
When $\beta=0.9$,
                   $\frac{1}{1-\beta} = \frac{1}{1-0.9} = 10$
So we are averaging over about 10 days.
When $\beta=0.98$,
                   $\frac{1}{1-\beta} = \frac{1}{1-0.98} = 50$
So we are averaging over about 50 days. Thus we will get a smoother curve as shown below.

An Example of Exponentially Weighted Average

The basic idea of gradient descent with momentum is to compute an exponentially weighted average of your gradients, and then use that gradient to update your weights instead. It almost always works faster than the standard gradient descent algorithm.


Gradient Descent with Momentum

Bias Correction

- Stands for Root Mean Square Propagation
- RMSprop is a batch learning algorithm similar to AdaGrad that aims to deal with radically diminishing learning rates.

- Many times, gradients may be tiny, and others may be huge, which makes learning difficult — trying to find a single global learning rate for the algorithm.
RMSprop looks at the step size that’s defined for that weight instead of the magnitude of the gradient. The step size adapts individually over time, so that we accelerate learning in the direction that we need. In this way, RMSProp mimics initializing an instance of AdaGrad in a locally convex bowl, allowing it to converge rapidly there

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