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Supervised Learning - Softmax Classfication (multi-variable 2) Softmax Classifiation : 주어진 입력에 따라 3개 이상의 class에서의 예측 (=Multi Classification) Linear : H(x) ∈ (-inf, inf) Softmax : H(x) ∈ [0,1] Step1) Hypothesis Step2) Cost function Step3) Training - gradient descent method Tensorflow w/ min-max scaling #load module import numpy as np import tensorflow as tf import matplotlib.pyplot as plt #input & label x_input = tf.constant([[25,22],[25,26],[25,30],[35,22],.. 2023. 8. 6.
Supervised Learning - Softmax Classification (multi-variable 1) Softmax Classifiation : 주어진 입력에 따라 3개 이상의 class에서의 예측 (=Multi Classification) Linear : H(x) ∈ (-inf, inf) Softmax : H(x) ∈ [0,1] Step1) Hypothesis Step2) Cost function Step3) Training - gradient descent method Numpy w/o min-max scaling #load module import numpy as np import matplotlib.pyplot as plt #input & label x_input = np.array([[1, 1], [2, 2.5], [2.5, 1.3], [4.3, 9.5], [5.5, 7.0], [6, 8.2],.. 2023. 8. 6.
Supervised Learning - Logistic Regression (multi-variable 2) Logistic Regression : 주어진 입력에 따라 discrete 한 값을 예측 (=Binary Classification) Linear : H(x) ∈ (-inf, inf) Logistic : H(x) ∈ [0,1] Hypothesis function 으로 logistic function (sigmoid function) 적용하여 regression 진행 입력에 따라서 0~1 사이 값 출력 application) 메일의 스팸여부, 영화 감상 긍정/부정, 주식 종목 구분 등 Step1) Hypothesis Step2) Cost function Step3) Training - gradient descent method Tensorflow w/ min-max scaling #load module i.. 2023. 8. 6.
Supervised Learning - Logistic Regression (multi-variable 1) Logistic Regression : 주어진 입력에 따라 discrete 한 값을 예측 (=Binary Classification) Linear : H(x) ∈ (-inf, inf) Logistic : H(x) ∈ [0,1] Hypothesis function 으로 logistic function (sigmoid function) 적용하여 regression 진행 입력에 따라서 0~1 사이 값 출력 application) 메일의 스팸여부, 영화 감상 긍정/부정, 주식 종목 구분 등 Step1) Hypothesis Step2) Cost function Step3) Training - gradient descent method Numpy w/o min-max scaling #load module impor.. 2023. 8. 5.

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