SDE

CNN : Convolutional layers . CNNs have unique layers called convolutional layers which separate them from RNNs and other neural networks. Within a convolutional layer, the input is transformed before being passed to the next layer. A CNN transforms the data by using filters.

RNN : Recurrent neural networks are networks that are designed to interpret temporal or sequential information. RNNs use other data points in a sequence to make better predictions. They do this by taking in input and reusing the activations of previous nodes or later nodes in the sequence to influence the output.

The main difference between CNN and RNN is the ability to process temporal information or data that comes in sequences, such as a sentence for example. Moreover, convolutional neural networks and recurrent neural networks are used for completely different purposes, and there are differences in the structures of the neural networks themselves to fit those different use cases.

CNNs employ filters within convolutional layers to transform data. Whereas, RNNs reuse activation functions from other data points in the sequence to generate the next output in a series.

Outlier values, or simply outliers, are data points in statistics that don’t belong to a certain population. An outlier value is an abnormal observation that is very much different from other values belonging to the set.

Identification of outlier values can be done by using univariate or some other graphical analysis method. Few outlier values can be assessed individually but assessing a large set of outlier values require the substitution of the same with either the 99th or the 1st percentile values.

There are two popular ways of treating outlier values:
1) To change the value so that it can be brought within a range
2) To simply remove the value

Ridge and Lasso regression uses two different penalty functions. Ridge uses L2 where as lasso go with L1. In ridge regression, the penalty is the sum of the squares of the coefficients and for the Lasso, it’s the sum of the absolute values of the coefficients. It’s a shrinkage towards zero using an absolute value (L1 penalty) rather than a sum of squares(L2 penalty).

As we know that ridge regression can’t have zero coefficients. Here, we can either select all the coefficients or none of them whereas LASSO does both parameter shrinkage and variable selection automatically because it zero out the co-efficients of collinear variables. Here it helps to select the variable(s) out of given n variables while performing lasso regression.

Fitting a time series forecasting model requires 5 steps . The steps are explained below :

1) Data preparation : Data preparation is usually the first step where we load all the essential packages and data into a time series object.

2) Time series decomposition : Decomposition basically means deconstructing and visualizing the series into its component parts.

3) Modelling : The actual model building is a simple 2-lines code using auto.arima() function. auto.arima will take care of the optimum parameter values, we just need to specify a few boolean parameters.

4) Forecasting : Making an actual forecast is the simplest of all the steps above . We are using forecast() function and passing the model above and specifying the number of time steps into the future we want to forecast.

5) Model evaluation : This is an extra step for model evaluation and accuracy tests .

Step 1 . I first sorted the array.
Step 2 . Then I assigned initial coins to bob so that he gets minimum.
Step 3 . Inside the loop, i give myself the first element and alice the second element, and increase counter of loop to 2 for every iteration.