参考试卷
一、写出以下单词的中文意思（每小题0.5分，共10分）
1
accuracy
11
customize
2
actuator
12
definition
3
adjust
13
defuzzification
4
agent
14
deployment
5
algorithm
15
effector
6
analogy
16
entity
7
attribute
17
extract
8
backtrack
18
feedback
9
blockchain
19
finite
10
cluster
20
framework
二、根据给出的中文意思，写出英文单词（每小题0.5分，共10分）
1
v.收集，搜集
11
n.神经元；神经细胞
2
adj.嵌入的，内置的
12
n.节点
3
n.指示器；指标
13
v.运转；操作
4
n.基础设施，基础架构
14
n.模式
5
v.合并；集成
15
v.察觉，发觉
6
n.解释器，解释程序
16
n.前提
7
n.迭代；循环accessible的固定短语
17
adj.程序的；过程的
8
n.库
18
n.回归
9
n.元数据
19
adj.健壮的，强健的；结实的
10
v.监视；控制；监测
20
v.筛选
三、根据给出的短语，写出中文意思（每小题1分，共10分）
1
data object
2
cyber security
3
smart manufacturing
4
clustered system
5
data visualization
6
open source
7
analyze text
8
cloud computing
9
computation power
10
object recognition
四、根据给出的中文意思，写出英文短语（每小题1分，共10分）
1
数据结构
2
决策树
3
演绎推理
4
贪婪最佳优先搜索
5
隐藏模式，隐含模式
6
知识挖掘
7
逻辑推理
8
预测性维护
9
搜索引擎
10
文本挖掘技术
五、写出以下缩略语的完整形式和中文意思（每小题1分，共10分）
缩略语
完整形式
中文意思
1
ANN
2
AR
3
BFS
4
CV
5
DFS
6
ES
7
IA
8
KNN
9
NLP
10
VR
六、阅读短文，回答问题（每小题2分，共10分）
Artificial Neural Network (ANN)
An artificial neural network (ANN) is the piece of a computing system designed to simulate the way the human brain analyzes and processes information. It is the foundation of artificial intelligence (AI) and solves problems that would prove impossible or difficult by human or statistical standards. ANNs have self-learning capabilities that enable them to produce better results as more data becomes available.
Artificial neural networks are built like the human brain, with neuron nodes interconnected like a web. The human brain has hundreds of billions of cells called neurons. Each neuron is made up of a cell body that is responsible for processing information by carrying information towards (inputs) and away (outputs) from the brain.
An ANN has hundreds or thousands of artificial neurons called processing units, which are interconnected by nodes. These processing units are made up of input and output units. The input units receive various forms and structures of information based on an internal weighting system, and the neural network attempts to learn about the information presented to produce one output report. Just like humans need rules and guidelines to com
e up with a result or output, ANNs also use a set of learning rules called backpropagation, an abbreviation for backward propagation of error, to perfect their output results.
An ANN initially goes through a training phase where it learns to recognize patterns in data, whether visually, aurally, or textually. During this supervised phase, the network compares its actual output produced with what it was meant to produce — the desired output. The difference between both outcomes is adjusted using backpropagation. This means that the network works backward, going from the output unit to the input units to adjust the weight of its connections between the units until the difference between the actual and desired outcome produces the lowest possible error.
A neural network may contain the following 3 layers:
Input layer – The activity of the input units represents the raw information that can feed into the network.
Hidden layer – To determine the activity of each hidden unit. The activities of the input units
and the weights on the connections between the input and the hidden units. There may be one or more hidden layers.
Output layer – The behavior of the output units depends on the activity of the hidden units and the weights between the hidden and output units.
1. What is an artificial neural network (ANN)?
2. What is each neuron made up of？
3. Wha do the input units do?
4. What does an ANN initially go through?
5. How many layers may a neural network contain? What are they?
七、将下列词填入适当的位置（每词只用一次）。（每小题10分，共20分）
填空题1
供选择的答案:
transactions
information
techniques
fraud
nodes
unstructured
subset
shared
automated
explosion
Deep Learning
1. What Is Deep Learning?
Deep learning is an artificial intelligence (AI) function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. Deep learning is a ___1___ of machine learning in artificial intelligence that has networks capable of learning unsupervised from data that is ___2___ or unlabeled. Also known as deep neural learning or deep neural network.
2. How Does Deep Learning Work?
Deep learning has evolved hand-in-hand with the digital era, which has brought about an _
__3___ of data in all forms and from every region of the world. This data, known simply as big data, is drawn from sources like social media, internet search engines, e-commerce platforms, and online cinemas, among others. This enormous amount of data is readily accessible and can be ___4___ through fintech applications like cloud computing.
However, the data, which normally is unstructured, is so vast that it could take decades for humans to comprehend it and extract relevant ___5___. Companies realize the incredible potential that can result from unraveling this wealth of information and are increasingly adapting to AI systems for ___6___ support.
3. Deep Learning vs. Machine Learning