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kolbaska11 [484]
3 years ago
11

Technology and Communications Quiz Active 1 Which of the following inventions has had the greatest impact on sound technology? A

. the extension cord
B. the tape recorder
c. the telegraph
d. the microphone​
Computers and Technology
1 answer:
liraira [26]3 years ago
6 0

Answer:

B the tape recorder bec half century later made sonorities not only reproducible but also alterable.

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Please check the answer section.

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Apex Legends combines royal genre products and hero-shooters for something fantastic like Overwatch. Please note that you've got one of the smoothest, snappest and most satisfying royal battle games since Fortnite and PUBG (the ex-callof duty developers) by the same team as the Titanfall franchise. Apex Legends is really characterized by its diverse range of characters and unique skills. While everyone is still subject to identical weapons, grenades and health packs, they can turn the tide of fighting quickly. Don't go in on your legends without brushing them. Here are all the characters ranked in Apex Legends.

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Lifeline. Lifeline.

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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.

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