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理解CNN、CNN可视化 CNN-Visualization

https://github.com/scutan90/CNN-Visualization

深度学习乳腺癌识别与诊断

https://github.com/scutan90/Breast-imaging-Deeplearning

英文版本

English version

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本项目是大家对AI的相关知识进行整合,集思广益,以便形成 内容充实,覆盖全面的文集。

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例子:

### 3.3.2 如何寻找超参数的最优值?(贡献者:大佬-斯坦福大学)

在使用机器学习算法时,总有一些难搞的超参数。例如权重衰减大小,高斯核宽度等等。算法不会设置这些参数,而是需要你去设置它们的值。设置的值对结果产生较大影响。常见设置超参数的做法有:

1. 猜测和检查:根据经验或直觉,选择参数,一直迭代。
2. 网格搜索:让计算机尝试在一定范围内均匀分布的一组值。
3. 随机搜索:让计算机随机挑选一组值。
4. 贝叶斯优化:使用贝叶斯优化超参数,会遇到贝叶斯优化算法本身就需要很多的参数的困难。
5. 在良好初始猜测的前提下进行局部优化:这就是 MITIE 的方法,它使用 BOBYQA 算法,并有一个精心选择的起始点。由于 BOBYQA 只寻找最近的局部最优解,所以这个方法是否成功很大程度上取决于是否有一个好的起点。在 MITIE 的情下,我们知道一个好的起点,但这不是一个普遍的解决方案,因为通常你不会知道好的起点在哪里。从好的方面来说,这种方法非常适合寻找局部最优解。稍后我会再讨论这一点。
6. 最新提出的 LIPO 的全局优化方法。这个方法没有参数,而且经验证比随机搜索方法好。

4. 贡献与项目概览

已提交MD版本章节:请查看MarkDown

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6. 友情链接

FlyAI-AI竞赛平台

7. 目录

第一章 数学基础 1

1.1 标量、向量、张量之间的联系 1
1.2 张量与矩阵的区别? 1
1.3 矩阵和向量相乘结果 1
1.4 向量和矩阵的范数归纳 1
1.5 如何判断一个矩阵为正定? 2
1.6 导数偏导计算 3
1.7 导数和偏导数有什么区别? 3
1.8 特征值分解与特征向量 3
1.9 奇异值与特征值有什么关系? 4
1.10 机器学习为什么要使用概率? 4
1.11 变量与随机变量有什么区别? 4
1.12 常见概率分布? 5
1.13 举例理解条件概率 9
1.14 联合概率与边缘概率联系区别? 10
1.15 条件概率的链式法则 10
1.16 独立性和条件独立性 11
1.17 期望、方差、协方差、相关系数总结 11

第二章 机器学习基础 14

2.1 各种常见算法图示 14 2.2 监督学习、非监督学习、半监督学习、弱监督学习? 15
2.3 监督学习有哪些步骤 16
2.4 多实例学习? 17
2.5 分类网络和回归的区别? 17
2.6 什么是神经网络? 17
2.7 常用分类算法的优缺点? 18
2.8 正确率能很好的评估分类算法吗? 20
2.9 分类算法的评估方法? 20
2.10 什么样的分类器是最好的? 22
2.11 大数据与深度学习的关系 22
2.12 理解局部最优与全局最优 23
2.13 理解逻辑回归 24
2.14 逻辑回归与朴素贝叶斯有什么区别? 24
2.15 为什么需要代价函数? 25
2.16 代价函数作用原理  25
2.17 为什么代价函数要非负? 26
2.18 常见代价函数? 26
2.19 为什么用交叉熵代替二次代价函数 28
2.20 什么是损失函数? 28
2.21 常见的损失函数 28
2.22 逻辑回归为什么使用对数损失函数? 30
0.00 对数损失函数是如何度量损失的? 31
2.23 机器学习中为什么需要梯度下降? 32
2.24 梯度下降法缺点? 32
2.25 梯度下降法直观理解? 32
2.23 梯度下降法算法描述? 33
2.24 如何对梯度下降法进行调优? 35
2.25 随机梯度和批量梯度区别? 35
2.26 各种梯度下降法性能比较 37
2.27 计算图的导数计算图解? 37
2.28 线性判别分析(LDA)思想总结 39
2.29 图解LDA核心思想 39
2.30 二类LDA算法原理? 40
2.30 LDA算法流程总结? 41
2.31 LDA和PCA区别? 41
2.32 LDA优缺点? 41
2.33 主成分分析(PCA)思想总结 42
2.34 图解PCA核心思想 42
2.35 PCA算法推理 43
2.36 PCA算法流程总结 44
2.37 PCA算法主要优缺点 45
2.38 降维的必要性及目的 45
2.39 KPCA与PCA的区别? 46
2.40 模型评估 47
2.40.1模型评估常用方法? 47
2.40.2 经验误差与泛化误差 47
2.40.3 图解欠拟合、过拟合 48
2.40.4 如何解决过拟合与欠拟合? 49
2.40.5 交叉验证的主要作用? 50
2.40.6 k折交叉验证? 50
2.40.7 混淆矩阵 50
2.40.8 错误率及精度 51
2.40.9 查准率与查全率 51
2.40.10 ROC与AUC 52
2.40.11 如何画ROC曲线? 53
2.40.12 如何计算TPR,FPR? 54
2.40.13 如何计算Auc? 56
2.40.14 为什么使用Roc和Auc评价分类器? 56
2.40.15 直观理解AUC 56
2.40.16 代价敏感错误率与代价曲线 57
2.40.17 模型有哪些比较检验方法 59
2.40.18 偏差与方差 59
2.40.19 为什么使用标准差? 60
2.40.20 点估计思想 61
2.40.21 点估计优良性原则? 61
2.40.22 点估计、区间估计、中心极限定理之间的联系? 62
2.40.23 类别不平衡产生原因? 62
2.40.24 常见的类别不平衡问题解决方法 62
2.41 决策树 64
2.41.1 决策树的基本原理 64
2.41.2 决策树的三要素? 64
2.41.3 决策树学习基本算法 65
2.41.4 决策树算法优缺点 65
2.40.5 熵的概念以及理解 66
2.40.6 信息增益的理解 66
2.40.7 剪枝处理的作用及策略? 67
2.41 支持向量机 67
2.41.1 什么是支持向量机 67
2.25.2 支持向量机解决的问题? 68
2.25.2 核函数作用? 69
2.25.3 对偶问题 69
2.25.4 理解支持向量回归 69
2.25.5 理解SVM(核函数) 69
2.25.6 常见的核函数有哪些? 69
2.25.6 软间隔与正则化 73
2.25.7 SVM主要特点及缺点? 73
2.26 贝叶斯 74
2.26.1 图解极大似然估计 74
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
2.26.4 朴素与半朴素贝叶斯分类器 76
2.26.5 贝叶斯网三种典型结构 76
2.26.6 什么是贝叶斯错误率 76
2.26.7 什么是贝叶斯最优错误率 76
2.27 EM算法解决问题及实现流程 76
2.28 为什么会产生维数灾难? 78
2.29 怎样避免维数灾难 82
2.30 聚类和降维有什么区别与联系? 82
2.31 GBDT和随机森林的区别 83
2.32 四种聚类方法之比较 84

第三章 深度学习基础 88

3.1 基本概念 88
3.1.1 神经网络组成? 88
3.1.2 神经网络有哪些常用模型结构? 90
3.1.3 如何选择深度学习开发平台? 92
3.1.4 为什么使用深层表示 92
3.1.5 为什么深层神经网络难以训练? 93
3.1.6 深度学习和机器学习有什么不同 94
3.2 网络操作与计算 95
3.2.1 前向传播与反向传播? 95
3.2.2 如何计算神经网络的输出? 97
3.2.3 如何计算卷积神经网络输出值? 98
3.2.4 如何计算Pooling层输出值输出值? 101
3.2.5 实例理解反向传播 102
3.3 超参数 105
3.3.1 什么是超参数? 105
3.3.2 如何寻找超参数的最优值? 105
3.3.3 超参数搜索一般过程? 106
3.4 激活函数 106
3.4.1 为什么需要非线性激活函数? 106
3.4.2 常见的激活函数及图像 107
3.4.3 常见激活函数的导数计算? 109
3.4.4 激活函数有哪些性质? 110
3.4.5 如何选择激活函数? 110
3.4.6 使用ReLu激活函数的优点? 111
3.4.7 什么时候可以用线性激活函数? 111
3.4.8 怎样理解Relu(<0时)是非线性激活函数? 111
3.4.9 Softmax函数如何应用于多分类? 112
3.5 Batch_Size 113
3.5.1 为什么需要Batch_Size? 113
3.5.2 Batch_Size值的选择 114
3.5.3 在合理范围内,增大 Batch_Size 有何好处? 114
3.5.4 盲目增大 Batch_Size 有何坏处? 114
3.5.5 调节 Batch_Size 对训练效果影响到底如何? 114
3.6 归一化 115
3.6.1 归一化含义? 115
3.6.2 为什么要归一化 115
3.6.3 为什么归一化能提高求解最优解速度? 115
3.6.4 3D图解未归一化 116
3.6.5 归一化有哪些类型? 117
3.6.6 局部响应归一化作用 117
3.6.7 理解局部响应归一化公式 117
3.6.8 什么是批归一化(Batch Normalization) 118
3.6.9 批归一化(BN)算法的优点 119
3.6.10 批归一化(BN)算法流程 119
3.6.11 批归一化和群组归一化 120
3.6.12 Weight Normalization和Batch Normalization 120
3.7 预训练与微调(fine tuning) 121
3.7.1 为什么无监督预训练可以帮助深度学习? 121
3.7.2 什么是模型微调fine tuning 121
3.7.3 微调时候网络参数是否更新? 122
3.7.4 fine-tuning模型的三种状态 122
3.8 权重偏差初始化 122
3.8.1 全都初始化为0 122
3.8.2 全都初始化为同样的值 123
3.8.3 初始化为小的随机数 124
3.8.4 用1/sqrt(n)校准方差 125
3.8.5 稀疏初始化(Sparse Initialazation) 125
3.8.6 初始化偏差 125
3.9 Softmax 126
3.9.1 Softmax定义及作用 126
3.9.2 Softmax推导 126
3.10 理解One Hot Encodeing原理及作用? 126
3.11 常用的优化器有哪些 127
3.12 Dropout 系列问题 128
3.12.1 dropout率的选择 128
3.27 Padding 系列问题 128

第四章 经典网络 129

4.1 LetNet5 129
4.1.1 模型结构 129
4.1.2 模型结构 129
4.1.3 模型特性 131
4.2 AlexNet 131
4.2.1 模型结构 131
4.2.2 模型解读 131
4.2.3 模型特性 135
4.3 可视化ZFNet-解卷积 135
4.3.1 基本的思想及其过程 135
4.3.2 卷积与解卷积 136
4.3.3 卷积可视化 137
4.3.4 ZFNe和AlexNet比较 139
4.4 VGG 140
4.1.1 模型结构 140
4.1.2 模型特点 140
4.5 Network in Network 141
4.5.1 模型结构 141
4.5.2 模型创新点 141
4.6 GoogleNet 143
4.6.1 模型结构 143
4.6.2 Inception 结构 145
4.6.3 模型层次关系 146
4.7 Inception 系列 148
4.7.1 Inception v1 148
4.7.2 Inception v2 150
4.7.3 Inception v3 153
4.7.4 Inception V4 155
4.7.5 Inception-ResNet-v2 157
4.8 ResNet及其变体 158
4.8.1 重新审视ResNet 159
4.8.2 残差块 160
4.8.3 ResNet架构 162
4.8.4 残差块的变体 162
4.8.5 ResNeXt 162
4.8.6 Densely Connected CNN 164
4.8.7 ResNet作为小型网络的组合 165
4.8.8 ResNet中路径的特点 166
4.9 为什么现在的CNN模型都是在GoogleNet、VGGNet或者AlexNet上调整的? 167

第五章 卷积神经网络(CNN) 170

5.1 卷积神经网络的组成层 170
5.2 卷积如何检测边缘信息? 171
5.2 卷积的几个基本定义? 174
5.2.1 卷积核大小 174
5.2.2 卷积核的步长 174
5.2.3 边缘填充 174
5.2.4 输入和输出通道 174
5.3 卷积网络类型分类? 174
5.3.1 普通卷积 174
5.3.2 扩张卷积 175
5.3.3 转置卷积 176
5.3.4 可分离卷积 177
5.3 图解12种不同类型的2D卷积? 178
5.4 2D卷积与3D卷积有什么区别? 181
5.4.1 2D 卷积 181
5.4.2 3D卷积 182
5.5 有哪些池化方法? 183
5.5.1 一般池化(General Pooling) 183
5.5.2 重叠池化(OverlappingPooling) 184
5.5.3 空金字塔池化(Spatial Pyramid Pooling) 184
5.6 1x1卷积作用? 186
5.7 卷积层和池化层有什么区别?  187
5.8 卷积核一定越大越好? 189
5.9 每层卷积只能用一种尺寸的卷积核? 189
5.10 怎样才能减少卷积层参数量? 190
5.11卷积操作时必须同时考虑通道和区域吗? 191
5.12 采用宽卷积的好处有什么?  192
5.12.1 窄卷积和宽卷积 192
5.12.2 为什么采用宽卷积? 192
5.13 卷积层输出的深度与哪个部件的个数相同?  192
5.14 如何得到卷积层输出的深度? 193
5.15 激活函数通常放在卷积神经网络的那个操作之后?  194
5.16 如何理解最大池化层有几分缩小? 194
5.17 理解图像卷积与反卷积 194
5.17.1 图像卷积 194
5.17.2 图像反卷积 196
5.18 不同卷积后图像大小计算? 198
5.18.1 类型划分 198
5.18.2 计算公式 199
5.19 步长、填充大小与输入输出关系总结? 199
5.19.1 没有0填充,单位步长 200
5.19.2 零填充,单位步长 200
5.19.3 不填充,非单位步长 202
5.19.4 零填充,非单位步长 202
5.20 理解反卷积和棋盘效应 204
5.20.1 为什么出现棋盘现象? 204
5.20.2 有哪些方法可以避免棋盘效应? 205
5.21 CNN主要的计算瓶颈? 207
5.22 CNN的参数经验设置 207
5.23 提高泛化能力的方法总结 208
5.23.1 主要方法 208
5.23.2 实验证明 208
5.24 CNN在CV与NLP领域运用的联系与区别? 213
5.24.1 联系 213
5.24.2 区别 213
5.25 CNN凸显共性的手段? 213
5.25.1 局部连接 213
5.25.2 权值共享 214
5.25.3 池化操作 215
5.26 全卷积与Local-Conv的异同点 215
5.27 举例理解Local-Conv的作用 215
5.28 简述卷积神经网络进化史 216

第六章 循环神经网络(RNN) 218

6.1 RNNs和FNNs有什么区别? 218
6.2 RNNs典型特点? 218
6.3 RNNs能干什么? 219
6.4 RNNs在NLP中典型应用? 220
6.5 RNNs训练和传统ANN训练异同点? 220
6.6 常见的RNNs扩展和改进模型 221
6.6.1 Simple RNNs(SRNs) 221
6.6.2 Bidirectional RNNs 221
6.6.3 Deep(Bidirectional) RNNs 222
6.6.4 Echo State Networks(ESNs) 222
6.6.5 Gated Recurrent Unit Recurrent Neural Networks 224
6.6.6 LSTM Netwoorks 224
6.6.7 Clockwork RNNs(CW-RNNs) 225

第七章 目标检测 228

7.1 基于候选区域的目标检测器 228
7.1.1 滑动窗口检测器 228
7.1.2 选择性搜索 229
7.1.3 R-CNN 230
7.1.4 边界框回归器 230
7.1.5 Fast R-CNN 231
7.1.6 ROI 池化 233
7.1.7 Faster R-CNN 233
7.1.8 候选区域网络 234
7.1.9 R-CNN 方法的性能 236
7.2 基于区域的全卷积神经网络(R-FCN) 237
7.3 单次目标检测器 240
7.3.1单次检测器 241
7.3.2 滑动窗口进行预测 241
7.3.3 SSD 243
7.4 YOLO系列 244
7.4.1 YOLOv1介绍 244
7.4.2 YOLOv1模型优缺点? 252
7.4.3 YOLOv2 253
7.4.4 YOLOv2改进策略 254
7.4.5 YOLOv2的训练 261
7.4.6 YOLO9000 261
7.4.7 YOLOv3 263
7.4.8 YOLOv3改进 264

第八章 图像分割 269

8.1 传统的基于CNN的分割方法缺点? 269
8.1 FCN 269
8.1.1 FCN改变了什么? 269
8.1.2 FCN网络结构? 270
8.1.3 全卷积网络举例? 271
8.1.4 为什么CNN对像素级别的分类很难? 271
8.1.5 全连接层和卷积层如何相互转化? 272
8.1.6 FCN的输入图片为什么可以是任意大小? 272
8.1.7 把全连接层的权重W重塑成卷积层的滤波器有什么好处? 273
8.1.8 反卷积层理解 275
8.1.9 跳级(skip)结构 276
8.1.10 模型训练 277
8.1.11 FCN缺点 280
8.2 U-Net 280
8.3 SegNet 282
8.4 空洞卷积(Dilated Convolutions) 283
8.4 RefineNet 285
8.5 PSPNet 286
8.6 DeepLab系列 288
8.6.1 DeepLabv1 288
8.6.2 DeepLabv2 289
8.6.3 DeepLabv3 289
8.6.4 DeepLabv3+ 290
8.7 Mask-R-CNN 293
8.7.1 Mask-RCNN 的网络结构示意图 293
8.7.2 RCNN行人检测框架 293
8.7.3 Mask-RCNN 技术要点 294
8.8 CNN在基于弱监督学习的图像分割中的应用 295
8.8.1 Scribble标记 295
8.8.2 图像级别标记 297
8.8.3 DeepLab+bounding box+image-level labels 298
8.8.4 统一的框架 299

第九章 强化学习 301

9.1 强化学习的主要特点? 301
9.2 强化学习应用实例 302
9.3 强化学习和监督式学习、非监督式学习的区别 303
9.4 强化学习主要有哪些算法? 305
9.5 深度迁移强化学习算法 305
9.6 分层深度强化学习算法 306
9.7 深度记忆强化学习算法 306
9.8 多智能体深度强化学习算法 307
9.9 深度强化学习算法小结 307

第十章 迁移学习 309

10.1 什么是迁移学习? 309
10.2 什么是多任务学习? 309
10.3 多任务学习有什么意义? 309
10.4 什么是端到端的深度学习? 311
10.5 端到端的深度学习举例? 311
10.6 端到端的深度学习有什么挑战? 311
10.7 端到端的深度学习优缺点? 312

第十三章 优化算法 314

13.1 CPU和GPU 的区别? 314
13.2 如何解决训练样本少的问题 315
13.3 什么样的样本集不适合用深度学习? 315
13.4 有没有可能找到比已知算法更好的算法? 316
13.5 何为共线性, 跟过拟合有啥关联? 316
13.6 广义线性模型是怎被应用在深度学习中? 316
13.7 造成梯度消失的原因? 317
13.8 权值初始化方法有哪些 317
13.9 启发式优化算法中,如何避免陷入局部最优解? 318
13.10 凸优化中如何改进GD方法以防止陷入局部最优解 319
13.11 常见的损失函数? 319
13.14 如何进行特征选择(feature selection)? 321
13.14.1 如何考虑特征选择 321
13.14.2 特征选择方法分类 321
13.14.3 特征选择目的 322
13.15 梯度消失/梯度爆炸原因,以及解决方法 322
13.15.1 为什么要使用梯度更新规则? 322
13.15.2 梯度消失、爆炸原因? 323
13.15.3 梯度消失、爆炸的解决方案 324
13.16 深度学习为什么不用二阶优化 325
13.17 怎样优化你的深度学习系统? 326
13.18为什么要设置单一数字评估指标? 326
13.19满足和优化指标(Satisficing and optimizing metrics) 327
13.20 怎样划分训练/开发/测试集 328
13.21如何划分开发/测试集大小 329
13.22什么时候该改变开发/测试集和指标? 329
13.23 设置评估指标的意义? 330
13.24 什么是可避免偏差? 331
13.25 什么是TOP5错误率? 331
13.26 什么是人类水平错误率? 332
13.27 可避免偏差、几大错误率之间的关系? 332
13.28 怎样选取可避免偏差及贝叶斯错误率? 332
13.29 怎样减少方差? 333
13.30 贝叶斯错误率的最佳估计 333
13.31 举机器学习超过单个人类表现几个例子? 334
13.32 如何改善你的模型? 334
13.33 理解误差分析 335
13.34 为什么值得花时间查看错误标记数据? 336
13.35 快速搭建初始系统的意义? 336
13.36 为什么要在不同的划分上训练及测试? 337
13.37 如何解决数据不匹配问题? 338
13.38 梯度检验注意事项? 340
13.39 什么是随机梯度下降? 341
13.40 什么是批量梯度下降? 341
13.41 什么是小批量梯度下降? 341
13.42 怎么配置mini-batch梯度下降 342
13.43 局部最优的问题 343
13.44 提升算法性能思路 346

第十四章 超参数调整 358

14.1 调试处理 358
14.2 有哪些超参数 359
14.3 如何选择调试值? 359
14.4 为超参数选择合适的范围 359
14.5 如何搜索超参数? 359

第十五章 异构计算, GPU和框架选型指南 361

15.1 什么是异构计算? 361
15.2 什么是GPGPU? 361
15.3 GPU架构简介 361
15.3.1 为什么要使用GPU?
15.3.2 CUDA 核心是什么?
15.3.3 新图灵架构里的tensor core对深度学习有什么作用?
15.3.4 GPU内存架构和应用性能的联系?
15.4 CUDA 框架
15.4.1 做CUDA编程难不难?
15.4.2 cuDNN
15.5 GPU硬件环境配置推荐
15.5.1 GPU主要性能指标
15.5.2 购买建议
15.6 软件环境搭建
15.6.1 操作系统选择?
15.6.2 本机安装还是使用docker?
15.6.3 GPU驱动问题
15.7 框架选择
15.7.1 主流框架比较
15.7.2 框架详细信息
15.7.3 哪些框架对于部署环境友好?
15.7.4 移动平台的框架如何选择?
15.8 其他
15.8.1 多GPU环境的配置
15.8.2 是不是可以分布式训练?
15.8.3 可以在SPARK环境里训练或者部署模型吗?
15.8.4 怎么进一步优化性能?
15.8.5 TPU和GPU的区别?
15.8.6 未来量子计算对于深度学习等AI技术的影响?

参考文献 366

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