:)

Building Deep Neural Network● Initialization○ 2-Layer Neural Networkdef initialize_parameters(n_x, n_h, n_y): np.random.seed(1) W1 = np.random.randn(n_h, n_x) * 0.01 b1 = np.zeros((n_h, 1)) W2 = np.random.randn(n_y, n_h) * 0.01 b2 = np.zeros((n_y, 1)) parameters = {"W1": W1, "b1": b1, "W2": W2, "b2": b2} return..

Planar data classification with one hidden layerimport numpy as npimport copyimport matplotlib.pyplot as pltfrom testCases_v2 import *from public_tests import *import sklearnimport sklearn.datasetsimport sklearn.linear_modelfrom planar_utils import plot_decision_boundary, sigmoid, load_planar_dataset, load_extra_datasets%matplotlib inline%load_ext autoreload%autoreload 2# 데이터 불러오기X, Y = load_pla..

고양이를 인식하는 로지스틱 회귀 분류기import numpy as npimport copyimport matplotlib.pyplot as pltimport h5pyimport scipyfrom PIL import Imagefrom scipy import ndimagefrom lr_utils import load_datasetfrom public_tests import *%matplotlib inline%load_ext autoreload%autoreload 2 데이터 개요 및 전처리# Loading the data (cat/non-cat)train_set_x_orig, train_set_y, test_set_x_orig, test_set_y, classes = load_dataset()# Example..