深度学习试题500问.docx
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深度学习试题500问
深度学习试题500问
1.1标量、向量、张量之间的联系1[填空题]
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1.2张量与矩阵的区别?
1[填空题]
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1.3矩阵和向量相乘结果1[填空题]
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1.4向量和矩阵的范数归纳1[填空题]
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1.5如何判断一个矩阵为正定?
2[填空题]
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1.6导数偏导计算3[填空题]
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1.7导数和偏导数有什么区别?
3[填空题]
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1.8特征值分解与特征向量3[填空题]
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1.9奇异值与特征值有什么关系?
4[填空题]
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1.10机器学习为什么要使用概率?
4[填空题]
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1.11变量与随机变量有什么区别?
4[填空题]
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1.12常见概率分布?
5[填空题]
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1.13举例理解条件概率9[填空题]
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1.14联合概率与边缘概率联系区别?
10[填空题]
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1.15条件概率的链式法则10[填空题]
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1.16独立性和条件独立性11[填空题]
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1.17期望、方差、协方差、相关系数总结11[填空题]*
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2.1各种常见算法图示14[填空题]
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2.2监督学习、非监督学习、半监督学习、弱监督学习?
15[填空题]
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2.3监督学习有哪些步骤16[填空题]
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2.4多实例学习?
17[填空题]
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2.5分类网络和回归的区别?
17[填空题]
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2.6什么是神经网络?
17[填空题]
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2.7常用分类算法的优缺点?
18[填空题]
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2.8正确率能很好的评估分类算法吗?
20[填空题]
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2.9分类算法的评估方法?
20[填空题]
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2.10什么样的分类器是最好的?
22[填空题]
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2.11大数据与深度学习的关系22[填空题]
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2.12理解局部最优与全局最优23[填空题]
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2.13理解逻辑回归24[填空题]
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2.14逻辑回归与朴素贝叶斯有什么区别?
24[填空题]
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2.15为什么需要代价函数?
25[填空题]
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2.16代价函数作用原理 25[填空题]
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2.17为什么代价函数要非负?
26[填空题]
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2.18常见代价函数?
26[填空题]
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2.19为什么用交叉熵代替二次代价函数28[填空题]
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2.20什么是损失函数?
28[填空题]
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2.21常见的损失函数28[填空题]
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2.22逻辑回归为什么使用对数损失函数?
30[填空题]
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0.00对数损失函数是如何度量损失的?
31[填空题]
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2.23机器学习中为什么需要梯度下降?
32[填空题]
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2.24梯度下降法缺点?
32[填空题]
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2.25梯度下降法直观理解?
32[填空题]
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2.23梯度下降法算法描述?
33[填空题]
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2.24如何对梯度下降法进行调优?
35[填空题]
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2.25随机梯度和批量梯度区别?
35[填空题]
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2.26各种梯度下降法性能比较37[填空题]
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2.27计算图的导数计算图解?
37[填空题]
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2.28线性判别分析(LDA)思想总结39[填空题]
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2.29图解LDA核心思想39[填空题]
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2.30二类LDA算法原理?
40[填空题]
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2.30LDA算法流程总结?
41[填空题]
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2.31LDA和PCA区别?
41[填空题]
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2.32LDA优缺点?
41[填空题]
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2.33主成分分析(PCA)思想总结42[填空题]
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2.34图解PCA核心思想42[填空题]
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2.35PCA算法推理43[填空题]
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2.36PCA算法流程总结44[填空题]
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2.37PCA算法主要优缺点45[填空题]
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2.38降维的必要性及目的45[填空题]
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2.39KPCA与PCA的区别?
46[填空题]
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2.40模型评估47[填空题]
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2.40.1模型评估常用方法?
47[填空题]
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2.40.2经验误差与泛化误差47[填空题]
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2.40.3图解欠拟合、过拟合48[填空题]
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2.40.4如何解决过拟合与欠拟合?
49[填空题]
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2.40.5交叉验证的主要作用?
50[填空题]
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2.40.6k折交叉验证?
50[填空题]
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2.40.7混淆矩阵50[填空题]
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2.40.8错误率及精度51[填空题]
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2.40.9查准率与查全率51[填空题]
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2.40.10ROC与AUC52[填空题]
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2.40.11如何画ROC曲线?
53[填空题]
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2.40.12如何计算TPR,FPR?
54[填空题]
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2.40.13如何计算Auc?
56[填空题]
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2.40.14为什么使用Roc和Auc评价分类器?
56[填空题]
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2.40.15直观理解AUC56[填空题]
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2.40.16代价敏感错误率与代价曲线57[填空题]
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2.40.17模型有哪些比较检验方法59[填空题]
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2.40.18偏差与方差59[填空题]
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2.40.19为什么使用标准差?
60[填空题]
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2.40.20点估计思想61[填空题]
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2.40.21点估计优良性原则?
61[填空题]
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2.40.22点估计、区间估计、中心极限定理之间的联系?
62[填空题]
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2.40.23类别不平衡产生原因?
62[填空题]
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2.40.24常见的类别不平衡问题解决方法62[填空题]
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2.41决策树64[填空题]
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2.41.1决策树的基本原理64[填空题]
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2.41.2决策树的三要素?
64[填空题]
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2.41.3决策树学习基本算法65[填空题]
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2.41.4决策树算法优缺点65[填空题]
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2.40.5熵的概念以及理解66[填空题]
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2.40.6信息增益的理解66[填空题]
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2.40.7剪枝处理的作用及策略?
67[填空题]
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2.41支持向量机67[填空题]
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2.41.1什么是支持向量机67[填空题]
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2.25.2支持向量机解决的问题?
68[填空题]
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2.25.2核函数作用?
69[填空题]
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2.25.3对偶问题69[填空题]
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2.25.4理解支持向量回归69[填空题]
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2.25.5理解SVM(核函数)69[填空题]
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2.25.6常见的核函数有哪些?
69[填空题]
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2.25.6软间隔与正则化73[填空题]
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2.25.7SVM主要特点及缺点?
73[填空题]
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2.26贝叶斯74[填空题]
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2.26.1图解极大似然估计74[填空题]
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2.26.2朴素贝叶斯分类器和一般的贝叶斯分类器有什么区别?
76[填空题]
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2.26.4朴素与半朴素贝叶斯分类器76[填空题]
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2.26.5贝叶斯网三种典型结构76[填空题]
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2.26.6什么是贝叶斯错误率76[填空题]
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2.26.7什么是贝叶斯最优错误率76[填空题]
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2.27EM算法解决问题及实现流程76[填空题]
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2.28为什么会产生维数灾难?
78[填空题]
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2.29怎样避免维数灾难82[填空题]
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2.30聚类和降维有什么区别与联系?
82[填空题]
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2.31GBDT和随机森林的区别83[填空题]
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2.32四种聚类方法之比较84[填空题]*
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3.1基本概念88[填空题]
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3.1.1神经网络组成?
88[填空题]
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3.1.2神经网络有哪些常用模型结构?
90[填空题]
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3.1.3如何选择深度学习开发平台?
92[填空题]
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3.1.4为什么使用深层表示92[填空题]
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3.1.5为什么深层神经网络难以训练?
93[填空题]
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3.1.6深度学习和机器学习有什么不同94[填空题]
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3.2网络操作与计算95[填空题]
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3.2.1前向传播与反向传播?
95[填空题]
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3.2.2如何计算神经网络的输出?
97[填空题]
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3.2.3如何计算卷积神经网络输出值?
98[填空题]
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3.2.4如何计算Pooling层输出值输出值?
101[填空题]
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3.2.5实例理解反向传播102[填空题]
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3.3超参数105[填空题]
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3.3.1什么是超参数?
105[填空题]
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3.3.2如何寻找超参数的最优值?
105[填空题]
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3.3.3超参数搜索一般过程?
106[填空题]
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3.4激活函数106[填空题]
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3.4.1为什么需要非线性激活函数?
106[填空题]
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3.4.2常见的激活函数及图像107[填空题]
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3.4.3常见激活函数的导数计算?
109[填空题]
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3.4.4激活函数有哪些性质?
110[填空题]
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3.4.5如何选择激活函数?
110[填空题]
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3.4.6使用ReLu激活函数的优点?
111[填空题]
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3.4.7什么时候可以用线性激活函数?
111[填空题]
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3.4.8怎样理解Relu(<0时)是非线性激活函数?
111[填空题]
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3.4.9Softmax函数如何应用于多分类?
112[填空题]
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3.5Batch_Size113[填空题]
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3.5.1为什么需要Batch_Size?
113[填空题]
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3.5.2Batch_Size值的选择114[填空题]
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3.5.3在合理范围内,增大Batch_Size有何好处?
114[填空题]
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3.5.4盲目增大Batch_Size有何坏处?
114[填空题]
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3.5.5调节Batch_Size对训练效果影响到底如何?
114[填空题]
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3.6归一化115[填空题]
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3.6.1归一化含义?
115[填空题]
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3.6.2为什么要归一化115[填空题]
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3.6.3为什么归一化能提高求解最优解速度?
115[填空题]
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3.6.43D图解未归一化116[填空题]
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3.6.5归一化有哪些类型?
117[填空题]
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3.6.6局部响应归一化作用117[填空题]
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3.6.7理解局部响应归一化公式117[填空题]
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3.6.8什么是批归一化(BatchNormalization)118[填空题]
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3.6.9批归一化(BN)算法的优点119[填空题]
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3.6.10批归一化(BN)算法流程119[填空题]
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