Help! Picture provided

Computers and Technology

2 answers:

natka813 [3]2 years ago

7 0

The answer is c
hope this helps

Mrac [35]2 years ago

7 0

Answer:

C. Make suggestions to the director about plot

Explanation:

A video producer is generally expected to: Create and develop multimedia (audio/video) content for customer stories, internal company announcement videos, external promotional social videos, product marketing videos, recruiting videos, corporate training and other enterprise-wide projects.

You might be interested in

The function below takes a single parameter number_list which is a list that can contain integers and floats. Complete the funct

Andreas93 [3]

Answer:

def getLargest(number_list):
new_list = []
for x in number_list:
if(isinstance(x, int)):
new_list.append(x)
largest = max(new_list)
return largest

Explanation:

Firstly, create a function getLargest() that take one input parameter, number_list.

The function will filter out the float type number from the list by using isinstance() method (Line 5). This method will check if a current x value is an integer. If so, the x value will be added to new_list.

Next, use Python built-in max function to get the largest integer from the new_list and return it as output.

6 0

3 years ago

An organization is concerned with the amount of sensor data that is being generated locally, analyzed in the cloud, and returned

Sonja [21]

Answer:

Fog computing

Explanation:

Fog computing or fogging is an decentralized architecture that resides between the cloud and the data source. Fog brings the cloud closer to the data source, that is, it brings resource exploitation, security polices and operational cost closer to the data source.

In our scenario, with fogging in place, the generated sensor data will be preprocessed closer to the local site. The data will preprocessed faster and the quality of the processed data will be greatly increased.

3 0

3 years ago

In order to consolidate your theoretical knowledge into technique and skills with practical and applicational value, you will us

Arisa [49]

Answer:

Check the explanation

Explanation:

Lasso: R example

To run Lasso Regression you can re-use the glmnet() function, but with the alpha parameter set to 1.

# Perform 10-fold cross-validation to select lambda --------------------------- lambdas_to_try <- 10^seq(-3, 5, length.out = 100) # Setting alpha = 1 implements lasso regression lasso_cv <- cv.glmnet(X, y, alpha = 1, lambda = lambdas_to_try, standardize = TRUE, nfolds = 10) # Plot cross-validation results plot(lasso_cv)

Best cross-validated lambda lambda_cv <- lasso_cv$lambda.min # Fit final model, get its sum of squared residuals and multiple R-squared model_cv <- glmnet(X, y, alpha = 1, lambda = lambda_cv, standardize = TRUE) y_hat_cv <- predict(model_cv, X) ssr_cv <- t(y - y_hat_cv) %*% (y - y_hat_cv) rsq_lasso_cv <- cor(y, y_hat_cv)^2 # See how increasing lambda shrinks the coefficients -------------------------- # Each line shows coefficients for one variables, for different lambdas. # The higher the lambda, the more the coefficients are shrinked towards zero. res <- glmnet(X, y, alpha = 1, lambda = lambdas_to_try, standardize = FALSE) plot(res, xvar = "lambda") legend("bottomright", lwd = 1, col = 1:6, legend = colnames(X), cex = .7)

Kindly check the Image below.