목록전체 글 (74)
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[논문 읽기] Why Should I Trust You?": Explaining the Predictions of Any Classifier
https://arxiv.org/abs/1602.04938 "Why Should I Trust You?": Explaining the Predictions of Any Classifier Despite widespread adoption, machine learning models remain mostly black boxes. Understanding the reasons behind predictions is, however, quite important in assessing trust, which is fundamental if one plans to take action based on a prediction, or when ch arxiv.org Abstract 대부분의 ML 모델은 Black..
[논문 읽기] Effectivs Reinforcement Learning through Evolutionary Surrogate-Assisted Prescription
https://arxiv.org/abs/2002.05368 Effective Reinforcement Learning through Evolutionary Surrogate-Assisted Prescription There is now significant historical data available on decision making in organizations, consisting of the decision problem, what decisions were made, and how desirable the outcomes were. Using this data, it is possible to learn a surrogate model, and with arxiv.org Before RL(Rei..
[Python] pytorch와 sklearn의 train_test_split 활용하여 데이터 셋 나누기와 간단한 CNN
import torch from torchvision import datasets import torchvision.transforms as transforms import os import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split import torch.nn as nn import torch.optim as optim from torchvision.datasets import STL10 from torch.utils.data import DataLoader #\를 그대로 사용 path2data = r'.\dataset' #폴더 없을 경우 생성 if not os.path.exists(path2data): o..
[Python] Selenium과 bs4를 이용한 크롤링 + Pyautogui
크롤링 기본 구조 1. Selenium을 사용하여 소스코드로 지정된 WebDriver를 실행하여 웹 페이지에 접속한다. 2. 접속한 웹 페이지를 HTML 소스코드 형태로 파싱한다. 3. 파싱된 HTML 전체 코드에서 Beautiful Soup를 사용하여 원하는 부분만 골라낸다. 4. 골라낸 데이터를 원하는 형식의 파일로 저장한다. 크롬 버전확인 및 드라이버 설정 https://googlechromelabs.github.io/chrome-for-testing/ Chrome for Testing availability chrome-headless-shellmac-arm64https://edgedl.me.gvt1.com/edgedl/chrome/chrome-for-testing/118.0.5951.0/mac-..