哪吒打字编程打字课少儿编程[人工智能]

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少儿编程

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i

ˌeɪ ˈaɪ

人工智能，能模仿人类思考的计算机程序

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o

b

o

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ˈrəʊbɒt

机器人，能按指令行动的机器

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e

a

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n

lɜːn

学习，让程序从数据中获取知识

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a

c

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tiːtʃ

教导，给程序输入信息让它学习

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ˈpætn

模式，数据中重复出现的规律

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ˈrekəɡnaɪz

识别，让程序认出图片、声音等

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f

y

ˈklæsɪfaɪ

分类，把东西按类别分开

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t

prɪˈdɪkt

预测，根据已知信息猜未来情况

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a

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a

ˈdeɪtə

数据，程序学习用的信息

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n

treɪn

训练，通过练习让程序变厉害

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ˈmɒdl

模型，程序里用来学习的部分

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m

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ˈɪmɪdʒ

图像，图片类的数据

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v

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e

vɔɪs

声音，说话的声音数据

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o

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m

a

n

d

kəˈmɑːnd

指令，告诉 ai 做什么的话

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p

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n

s

e

rɪˈspɒns

回应，ai 对指令的回答

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m

a

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t

smɑːt

智能的，能自己做判断的

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dɪˈsɪʒn

决定，ai 根据情况做的选择

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m

ˈælɡərɪðəm

算法，程序解决问题的步骤

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u

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ˈɪnput

输入，给 ai 的信息

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u

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p

u

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ˈaʊtput

输出，ai 给出的结果

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l

ˈleɪbl

标签，给数据做的分类标记

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u

r

e

ˈfiːtʃə

特征，事物的特点

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m

a

t

c

h

mætʃ

匹配，找相同或相似的东西

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o

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t

sɔːt

排序，按顺序排列东西

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ˈfɪltə

筛选，挑出符合条件的东西

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b

o

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ˈtʃætbɒt

聊天机器人，能对话的程序

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a

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feɪs

人脸，人的脸部图像

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p

e

e

c

h

spiːtʃ

语音，说出来的话

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e

x

t

tekst

文本，文字类的数据

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q

u

e

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t

i

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n

ˈkwestʃən

问题，向 ai 提出的疑问

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n

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w

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r

ˈɑːnsə

答案，ai 对问题的回复

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m

e

m

o

r

y

ˈmeməri

记忆，ai 存储的信息

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d

a

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t

əˈdæpt

适应，ai 根据新情况改变

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ˈlɒdʒɪk

逻辑，思考的道理和规则

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b

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l

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ˌprɒbəˈbɪləti

可能性，事情发生的机会

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m

u

l

a

t

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ˈsɪmjuleɪt

模拟，模仿真实情况

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ˈfiːdbæk

反馈，告诉 ai 做得好不好

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c

u

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a

c

y

ˈækjərəsi

准确率，ai 做对的比例

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r

o

r

ˈerə

错误，ai 做错的情况

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l

a

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s

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f

y

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m

a

g

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让程序给图片分类的命令

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教机器人学习的命令

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v

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c

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让程序识别声音的命令

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e

x

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预测接下来情况的命令

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r

a

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m

o

d

e

l

训练学习模型的命令

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c

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p

a

t

t

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r

n

寻找相同模式的命令

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a

t

a

给数据排序的命令

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k

c

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向聊天机器人提问的命令

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给图片贴标签的命令

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[feis ˌrekəɡˈniʃən]

识别人脸的功能

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\u02c8a\u026a#\u4eba\u5de5\u667a\u80fd\uff0c\u80fd\u6a21\u4eff\u4eba\u7c7b\u601d\u8003\u7684\u8ba1\u7b97\u673a\u7a0b\u5e8f","robot#\u02c8r\u0259\u028ab\u0252t#\u673a\u5668\u4eba\uff0c\u80fd\u6309\u6307\u4ee4\u884c\u52a8\u7684\u673a\u5668","learn#l\u025c\u02d0n#\u5b66\u4e60\uff0c\u8ba9\u7a0b\u5e8f\u4ece\u6570\u636e\u4e2d\u83b7\u53d6\u77e5\u8bc6","teach#ti\u02d0t\u0283#\u6559\u5bfc\uff0c\u7ed9\u7a0b\u5e8f\u8f93\u5165\u4fe1\u606f\u8ba9\u5b83\u5b66\u4e60","pattern#\u02c8p\u00e6tn#\u6a21\u5f0f\uff0c\u6570\u636e\u4e2d\u91cd\u590d\u51fa\u73b0\u7684\u89c4\u5f8b","recognize#\u02c8rek\u0259\u0261na\u026az#\u8bc6\u522b\uff0c\u8ba9\u7a0b\u5e8f\u8ba4\u51fa\u56fe\u7247\u3001\u58f0\u97f3\u7b49","classify#\u02c8kl\u00e6s\u026afa\u026a#\u5206\u7c7b\uff0c\u628a\u4e1c\u897f\u6309\u7c7b\u522b\u5206\u5f00","predict#pr\u026a\u02c8d\u026akt#\u9884\u6d4b\uff0c\u6839\u636e\u5df2\u77e5\u4fe1\u606f\u731c\u672a\u6765\u60c5\u51b5","data#\u02c8de\u026at\u0259#\u6570\u636e\uff0c\u7a0b\u5e8f\u5b66\u4e60\u7528\u7684\u4fe1\u606f","train#tre\u026an#\u8bad\u7ec3\uff0c\u901a\u8fc7\u7ec3\u4e60\u8ba9\u7a0b\u5e8f\u53d8\u5389\u5bb3","model#\u02c8m\u0252dl#\u6a21\u578b\uff0c\u7a0b\u5e8f\u91cc\u7528\u6765\u5b66\u4e60\u7684\u90e8\u5206","image#\u02c8\u026am\u026ad\u0292#\u56fe\u50cf\uff0c\u56fe\u7247\u7c7b\u7684\u6570\u636e","voice#v\u0254\u026as#\u58f0\u97f3\uff0c\u8bf4\u8bdd\u7684\u58f0\u97f3\u6570\u636e","command#k\u0259\u02c8m\u0251\u02d0nd#\u6307\u4ee4\uff0c\u544a\u8bc9 ai \u505a\u4ec0\u4e48\u7684\u8bdd","response#r\u026a\u02c8sp\u0252ns#\u56de\u5e94\uff0cai \u5bf9\u6307\u4ee4\u7684\u56de\u7b54","smart#sm\u0251\u02d0t#\u667a\u80fd\u7684\uff0c\u80fd\u81ea\u5df1\u505a\u5224\u65ad\u7684","decision#d\u026a\u02c8s\u026a\u0292n#\u51b3\u5b9a\uff0cai \u6839\u636e\u60c5\u51b5\u505a\u7684\u9009\u62e9","algorithm#\u02c8\u00e6l\u0261\u0259r\u026a\u00f0\u0259m#\u7b97\u6cd5\uff0c\u7a0b\u5e8f\u89e3\u51b3\u95ee\u9898\u7684\u6b65\u9aa4","input#\u02c8\u026anput#\u8f93\u5165\uff0c\u7ed9 ai \u7684\u4fe1\u606f","output#\u02c8a\u028atput#\u8f93\u51fa\uff0cai \u7ed9\u51fa\u7684\u7ed3\u679c","label#\u02c8le\u026abl#\u6807\u7b7e\uff0c\u7ed9\u6570\u636e\u505a\u7684\u5206\u7c7b\u6807\u8bb0","feature#\u02c8fi\u02d0t\u0283\u0259#\u7279\u5f81\uff0c\u4e8b\u7269\u7684\u7279\u70b9","match#m\u00e6t\u0283#\u5339\u914d\uff0c\u627e\u76f8\u540c\u6216\u76f8\u4f3c\u7684\u4e1c\u897f","sort#s\u0254\u02d0t#\u6392\u5e8f\uff0c\u6309\u987a\u5e8f\u6392\u5217\u4e1c\u897f","filter#\u02c8f\u026alt\u0259#\u7b5b\u9009\uff0c\u6311\u51fa\u7b26\u5408\u6761\u4ef6\u7684\u4e1c\u897f","chatbot#\u02c8t\u0283\u00e6tb\u0252t#\u804a\u5929\u673a\u5668\u4eba\uff0c\u80fd\u5bf9\u8bdd\u7684\u7a0b\u5e8f","face#fe\u026as#\u4eba\u8138\uff0c\u4eba\u7684\u8138\u90e8\u56fe\u50cf","speech#spi\u02d0t\u0283#\u8bed\u97f3\uff0c\u8bf4\u51fa\u6765\u7684\u8bdd","text#tekst#\u6587\u672c\uff0c\u6587\u5b57\u7c7b\u7684\u6570\u636e","question#\u02c8kwest\u0283\u0259n#\u95ee\u9898\uff0c\u5411 ai \u63d0\u51fa\u7684\u7591\u95ee","answer#\u02c8\u0251\u02d0ns\u0259#\u7b54\u6848\uff0cai \u5bf9\u95ee\u9898\u7684\u56de\u590d","memory#\u02c8mem\u0259ri#\u8bb0\u5fc6\uff0cai \u5b58\u50a8\u7684\u4fe1\u606f","adapt#\u0259\u02c8d\u00e6pt#\u9002\u5e94\uff0cai \u6839\u636e\u65b0\u60c5\u51b5\u6539\u53d8","logic#\u02c8l\u0252d\u0292\u026ak#\u903b\u8f91\uff0c\u601d\u8003\u7684\u9053\u7406\u548c\u89c4\u5219","probability#\u02ccpr\u0252b\u0259\u02c8b\u026al\u0259ti#\u53ef\u80fd\u6027\uff0c\u4e8b\u60c5\u53d1\u751f\u7684\u673a\u4f1a","simulate#\u02c8s\u026amjule\u026at#\u6a21\u62df\uff0c\u6a21\u4eff\u771f\u5b9e\u60c5\u51b5","feedback#\u02c8fi\u02d0db\u00e6k#\u53cd\u9988\uff0c\u544a\u8bc9 ai \u505a\u5f97\u597d\u4e0d\u597d","accuracy#\u02c8\u00e6kj\u0259r\u0259si#\u51c6\u786e\u7387\uff0cai \u505a\u5bf9\u7684\u6bd4\u4f8b","error#\u02c8er\u0259#\u9519\u8bef\uff0cai \u505a\u9519\u7684\u60c5\u51b5","classify image##\u8ba9\u7a0b\u5e8f\u7ed9\u56fe\u7247\u5206\u7c7b\u7684\u547d\u4ee4","teach robot##\u6559\u673a\u5668\u4eba\u5b66\u4e60\u7684\u547d\u4ee4","recognize 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