공대생 도전 일지

import torch import torch.nn as nn import torch.nn.functional as f import torch.optim as optim from torch.utils.data import TensorDataset from torch.utils.data import DataLoader import numpy as np import matplotlib.pyplot as plt import pandas as pd from google.colab import drive drive.mount('/content/drive') data = pd.read_csv("/content/drive/MyDrive/Colab Notebooks/health.csv") h = torch.FloatT..

import tensorflow as tf import numpy as np import pandas as pd from keras.datasets.mnist import load_data from keras.models import Sequential, Model from keras.layers import Dense, Input ,Flatten, Dropout from keras.utils import plot_model, to_categorical from keras.regularizers import l2 from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt from google.colab impor..

import numpy as np import matplotlib.pyplot as plt import scipy.io # Setup the parameters you will use for this part of the exercise input_layer_size = 400 num_labels = 10 from google.colab import drive drive.mount('/content/drive') # Given code def displayData(X, example_width=None): if example_width is None: example_width = int(round(np.sqrt(X.shape[1]))) plt.set_cmap("gray") m, n = X.shape ex..

import numpy as np import matplotlib.pyplot as plt # ==================== Load Data ==================== # The first two columns contains the exam scores and the third column # contains the label. from google.colab import drive drive.mount('/content/drive') data = np.loadtxt('/content/drive/MyDrive/Colab Notebooks/ex2data2.txt', delimiter=',') data = np.array(data) X = data[:,0:2] y = data[:,2:3..

import numpy as np import matplotlib.pyplot as plt from google.colab import drive drive.mount('/content/drive') # ==================== Load Data ==================== # The first two columns contains the exam scores and the third column # contains the label. data = np.loadtxt('/content/drive/MyDrive/Colab Notebooks/ex2data1.txt', delimiter=',') data = np.array(data) X = data[:,0:2] y = data[:,2:3..

import numpy as np import matplotlib.pyplot as plt # ==================== Part 1: Basic Function ==================== print('Running warmUpExercise ... ') print('5x5 Identity Matrix: ') def warmUpExercise(): return np.eye(5)# YOUR CODE HERE warmUpExercise() output Running warmUpExercise ... 5x5 Identity Matrix: array([[1., 0., 0., 0., 0.], [0., 1., 0., 0., 0.], [0., 0., 1., 0., 0.], [0., 0., 0.,..