1.What does a neuron compute?(神經(jīng)元節(jié)點計算什么？)
A neuron computes a linear function (z = Wx + b) followed by an activation function(神經(jīng)元節(jié)點先計算線性函數(shù)（z = Wx + b），再計算激活。)
2、Which of these is the “Logistic Loss”?(下面哪一個是 Logistic 損失？
損失函數(shù)：𝐿(𝑦^(𝑖) , 𝑦(𝑖)) = ?𝑦(𝑖)log 𝑦^(𝑖) ? (1 ? 𝑦(𝑖))log(1 ? 𝑦^(𝑖))
3、Suppose img is a (32,32,3) array, representing a 32x32 image with 3 color channels red, green and blue. How do you reshape this into a column vector?(假設 img 是一個（32,32,3）數(shù)組，具有 3 個顏色通道：紅色、綠色和藍色的 32x32 像素的圖像。 如何將其重新轉(zhuǎn)換為列向量？)

x = img.reshape((32 * 32 * 3, 1))

4、Consider the two following random arrays “a” and “b”:(看一下下面的這兩個隨機數(shù)組“a”和“b”：)

a = np.random.randn(2, 3) # a.shape = (2, 3) b = np.random.randn(2, 1) # b.shape = (2, 1) c = a + b c.shape = (2, 3)

5、. Consider the two following random arrays “a” and “b”:(看一下下面的這兩個隨機數(shù)組“a”和“b”)

a = np.random.randn(4, 3) # a.shape = (4, 3) b = np.random.randn(3, 2) # b.shape = (3, 2) c = a * b

ab中表示元素乘法，是兩個矩陣之間對應的元素相乘，需要兩個矩陣之間的維數(shù)相同
6、Suppose you have 𝒏𝒙 input features per example. Recall that 𝑿 = [𝒙(𝟏), 𝒙(𝟐) … 𝒙(𝒎)]. What is the dimension of X?(假設你的每一個樣本有𝒏𝒙個輸入特征，想一下在 𝑿 = [𝒙(𝟏), 𝒙(𝟐) … 𝒙(𝒎)]中，X 的維度是多少？)
（𝒏𝒙,m)
7、Recall that np.dot(a,b) performs a matrix multiplication on a and b, whereas a*b performs
an element-wise multiplication.(回想一下，np.dot（a，b）在 a 和 b 上執(zhí)行矩陣乘法，而“a * b”執(zhí)行元素方式的乘法。)Consider the two following random arrays “a” and “b”:(看一下下面的這兩個隨機數(shù)組“a”和“b”：)

a = np.random.randn(12288, 150) # a.shape = (12288, 150) b = np.random.randn(150, 45) # b.shape = (150, 45) c = np.dot(a, b)

請問c的維度是多少？
解答：

import numpy as np a = np.random.randn(12288, 150) # a.shape = (12288, 150) b = np.random.randn(150, 45) # b.shape = (150, 45) c = np.dot(a, b) print(c.shape)

np.dot(a,b)計算的是元素的內(nèi)積，在線性代數(shù)中相當于矩陣的乘法。
8、看一下下面的這個代碼片段：請問要怎么把它們向量化？

# a.shape = (3,4) # b.shape = (4,1) for i in range(3):for j in range(4):c[i][j] = a[i][j] + b[j] c = a + b.T

9、看一下下面的代碼：請問 c 的維度會是多少？

a = np.random.randn(3, 3) b = np.random.randn(3, 1) c = a * b

c.shape = (3, 3)
這里將會使用廣播機制，b 會被復制三次，再使用元素乘法。
10、看一下下面的計算圖，J 輸出是什么：

J = u + v - w
= a * b + a * c - (b + c)
= a * (b + c) - (b + c)
= (a - 1) * (b + c)

總結(jié)

以上是生活随笔為你收集整理的吴恩达深度学习课程练习题汇总（第二周）的全部內(nèi)容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網(wǎng)站內(nèi)容還不錯，歡迎將生活随笔推薦給好友。