Write a code named gd. m to implement gradient descent algorithm for the logistic regression problem (1) using the constant step-size scheme. the input for gd. m should include xtr, ytr, yte, yte, the regularization parameter λ, and the constant step-size t. the output for gd. m should be the training error, test error and the objective value at each iteration.
the code should appropriately address two critical issues:
i) how do you choose the constant step-size t?
ii) when do you think to stop the iteration process in your gd algorithm such that its iterative output is guaranteed to be sufficiently close to the optimal solution?

How are natural numbers, whole numbers, integers, and rational numbers related

Fast sportcars the top speeds of sportscars, given in miles per hour, are: 155 mph bmw m5 217 mph lamborghini aventador spyder 205 mph ferrari 488 205 mph nissan gtr 197 mph chevrolet corvette stingray zr1 258 mph bugatti veyron supersport 195 mph dodge viper 270 mph hennessey venom 155 mph bmw m3 195 mph mercedes sl given: topspeeds=[155; 217; 205; 205; 197; 258; 195; 270; 155; 195]; carnames=string(["bmw m5" "lamborghini aventador spyder" "ferrari 488" "nissan gtr" "chevrolet corvette stingray zr1" "bugatti veyron supersport" "dodge viper" "hennessey venom" "bmw m3" "mercedes sl"]); the variable is a rectangular array. write a function called selectcars to identify cars with the top speed within a given range, and display the identified names. the selected cars speed will be in a range given by lowerbound < speed < upperbound. inputs to the function selectcars are: a column array os all car top speeds named topspeeds, the corresponding chara

This program reads a file called 'test.txt'. you are required to write two functions that build a wordlist out of all of the words found in the file and print all of the unique words found in the file. remove punctuations using 'string.punctuation' and 'strip()' before adding words to the wordlist. write a function build_wordlist() that takes a 'file pointer' as an argument and reads the contents, builds the wordlist after removing punctuations, and then returns the wordlist. another function find_unique() will take this wordlist as a parameter and return another wordlist comprising of all unique words found in the wordlist. example: contents of 'test.txt': test file another line in the test file output: ['another', 'file', 'in', 'line', 'test', 'the']