Python(27)
-
Anaconda Libraries
anaconda - numpy : conda install numpy - pandas : conda install pandas - sklearn : conda install -c anaconda scikit-learn - openCV : conda install -c menpo opencv3 - glob: 파일들의 목록을 뽑을 때 사용 : conda
2020.05.17 -
[Python] CodeSignal 문제 풀이 (58~60)
58. You are taking part in an Escape Room challenge designed specifically for programmers. In your efforts to find a clue, you've found a binary code written on the wall behind a vase, and realized that it must be an encrypted message. After some thought, your first guess is that each consecutive 8 bits of the code stand for the character with the corresponding extended ASCII code. Assuming that..
2020.04.27 -
[Python] CodeSignal 문제 풀이 (55~57)
55. Given a rectangular matrix containing only digits, calculate the number of different 2 × 2 squares in it. [Example] For the output should be differentSquares(matrix) = 6. Here are all 6 different 2 × 2 squares: 1 2 2 2 2 1 2 2 2 2 2 2 2 2 1 2 2 2 2 3 2 3 2 1 [Solution] # def differentSquares(matrix): l = [] total = 0 for i in range(len(matrix) - 1): for j in range(len(matrix[0]) - 1): arr = ..
2020.04.27 -
[Python] CodeSignal 문제 풀이 (52~54)
52. Define a word as a sequence of consecutive English letters. Find the longest word from the given string. [Example] For text = "Ready, steady, go!", the output should be longestWord(text) = "steady". [Solution] # def longestWord(text): l = list() print(ord("a"), ord("z"), ord("A"), ord("Z")) s = "" for i in range(len(text)): if 97
2020.04.14 -
[Python] CodeSignal 문제 풀이 (49~51)
49. Given a string, return its encoding defined as follows: First, the string is divided into the least possible number of disjoint substrings consisting of identical characters for example, "aabbbc" is divided into ["aa", "bbb", "c"] Next, each substring with length greater than one is replaced with a concatenation of its length and the repeating character for example, substring "bbb" is replac..
2020.04.14 -
모두를 위한 딥러닝 시즌2 (Deep Learning Zero To All)
김성훈 교수님의 모두를 위한 딥러닝 시즌2가 나왔습니다. https://www.youtube.com/channel/UCC76Jmsg6SAjdvphzGSJMBQ 이번 강의는 Tensorflow2.0과 Pytorch 2가지 버전으로 새롭게 나왔는데요. 이전 강의도 좋지만 이번 강의를 보시는 것도 좋을 것 같다고 생각됩니다. 모두 즐겁게 공부합시다!
2020.04.03