printf("ho_tari\n");

Breast Cancer Wisconsin Dataset Classification problem ◦ 10 input variables ◦ 1 binary output variable (benign or malignant)  569 data samples ◦ Use the first 100 samples as test set ◦ Use the next 100 samples as validation set ◦ Use the others as training set ◦ Use Numpy slicing. Do not use the “train_test_split” function. Data Preparation Download breast-cancer-wisconsin.data  Remove the row..

#include #include "stack.h" int main() { Stack s1(10); Stack s2(100); s1.push(100); //구조체 자료는 인자를 전달할 때 오버헤드를 줄이기 위해 포인터를 쓴다. s1.push(200); s1.push(300); std::cout

#include #include "array.h" int main() { std::cout

Problem #1. Problem #2.

from tensorflow.keras import layers, models from tensorflow.keras.datasets import mnist from tensorflow.keras import backend as K import numpy as np import matplotlib.pyplot as plt def Conv2D(filters, kernel_size, padding="same", activation="relu"): return layers.Conv2D(filters, kernel_size, padding=padding, activation=activation) class SCAE(models.Model): def __init__(self, org_shape=(1,28,28))..

from tensorflow.keras import layers, models class AE(models.Model): def __init__(self, x_nodes=784, z_dim=36): x_shape = (x_nodes,) x = layers.Input(shape=x_shape) z = layers.Dense(z_dim, activation='relu')(x) y = layers.Dense(x_nodes, activation='sigmoid')(z) # Essential parts: super().__init__(x, y) self.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) # Optional Par..

#include "list.h" #include #include int main(void) { List list; //insertFirstNode(&list, 4); // [4] list.insertFirstNode(4); list.printList(); //insertFirstNode(&list, 3); // [3, 4] list.insertFirstNode(3); list.printList(); //insertFirstNode(&list, 1); // [1, 3, 4] list.insertFirstNode(1); list.printList(); //insertNode(&list, 1, 2); // [1, 2, 3, 4] list.insertNode(1, 2); list.printList(); //de..