《TensorFlow神经网络编程》配书资源

本书首先简要介绍流行的TensorFlow库，并讲解如何用它训练不同的神经网络。 你将深入了解神经网络的基础知识和它背后的数学原理，以及为什么我们会选择TensorFlow训练神经网络。然后，你将实现一个简单的前馈神经网络。接下来，你将使用TensorFlow掌握神经网络的优化技术和算法，以及一些更复杂的神经网络的实现。最后，你将对如何利用TensorFlow的强大功能来训练各种复杂的神经网络有一个正确的理解。

封面图

目录

• 译者序
• 作者简介
• 审校者简介
• 前言
• 第1 章神经网络的数学原理 ················ 1
• 1.1理解线性代数 ···································· 1
• 1.1.1环境设置 ································ 2
• 1.1.2线性代数的数据结构 ············ 3
• 1.1.3线性代数运算 ························ 4
• 1.1.4求解线性方程 ······················· 9
• 1.1.5奇异值分解 ·························· 11
• 1.1.6特征值分解 ·························· 14
• 1.1.7主成分分析 ·························· 14
• 1.2微积分 ·············································· 15
• 1.2.1梯度 ······································ 16
• 1.2.2Hessian 矩阵 ························ 23
• 1.2.3行列式 ·································· 24
• 1.3最优化 ·············································· 25
• 1.4总结 ·················································· 28
• 第2 章深度前馈神经网络 ·················· 29
• 2.1定义前馈神经网络 ·························· 29
• 2.2理解反向传播 ·································· 30
• 2.3在TensorFlow 中实现前馈神经网络 ····································· 31
• 2.4分析Iris 数据集 ······························· 34
• 2.5使用前馈网络进行图像分类 ·········· 40
• 2.6总结 ·················································· 54
• 第3 章神经网络的优化 ······················· 55
• 3.1什么是优化 ······································ 55
• 3.2优化器的类型 ·································· 56
• 3.3梯度下降 ·········································· 57
• 3.3.1梯度下降的变体 ·················· 58
• 3.3.2优化梯度下降的算法 ·········· 59
• 3.4优化器的选择 ·································· 61
• 3.5总结 ·················································· 64
• 第4 章卷积神经网络 ··························· 65
• 4.1卷积神经网络概述和直观理解 ······ 66
• 4.1.1单个卷积层的计算 ·············· 66
• 4.1.2TensorFlow 中的CNN ········ 70
• 4.2卷积操作 ································· 72
• 4.2.1对图像进行卷积 ·················· 73
• 4.2.2步长 ······························ 75
• 4.3池化 ····································· 76
• 4.3.1最大池化 ······························ 77
• 4.3.2示例代码 ······························ 78
• 4.4使用卷积网络进行图像分类 ·········· 80
• 4.5总结 ································· 102
• 第5 章递归神经网络 ························· 103
• 5.1递归神经网络介绍 ························ 103
• 5.1.1RNN 实现 ·························· 105
• 5.1.2TensorFlow RNN 实现 ······ 110
• 5.2长短期记忆网络简介 ···················· 114
• 5.2.1LSTM 的生命周期 ············ 115
• 5.2.2LSTM 实现 ························ 117
• 5.3情感分析 ································ 122
• 5.3.1词嵌入 ································ 122
• 5.3.2使用RNN 进行情感分析 ························· 128
• 5.4总结 ·································· 134
• 第6 章生成模型 ·································· 135
• 6.1生成模型简介 ································ 135
• 6.1.1判别模型对生成模型 ········ 136
• 6.1.2生成模型的类型 ················ 137
• 6.2GAN ··································· 140
• 6.2.1GAN 示例 ·························· 141
• 6.2.2GAN 的种类 ······················ 150
• 6.3总结 ································· 152
• 第7 章深度信念网络 ························· 153
• 7.1理解深度信念网络 ························ 154
• 7.2训练模型 ································ 161
• 7.3标签预测 ································ 162
• 7.4探索模型的准确度 ························ 162
• 7.5DBN 在MNIST 数据集上的应用 ··· 163
• 7.5.1加载数据集 ························ 163
• 7.5.2具有256 个神经元的RBM层的DBN 的输入参数 ····· 163
• 7.5.3具有256 个神经元的RBM层的DBN 的输出 ············· 165
• 7.6DBN 中RBM 层的神经元数量的影响 ················· 165
• 7.6.1具有512 个神经元的RBM 层 ··················· 165
• 7.6.2具有128 个神经元的RBM 层 ··················· 166
• 7.6.3准确度指标对比 ················ 166
• 7.7具有两个RBM 层的DBN ············ 167
• 7.8用DBN 对NotMNIST 数据集进行分类 ····················· 169
• 7.9总结 ·································· 172
• 第8 章自编码器 ·································· 173
• 8.1自编码算法 ································ 174
• 8.2欠完备自编码器 ···························· 175
• 8.3数据集 ····································· 175
• 8.4基本自编码器 ································ 177
• 8.4.1自编码器的初始化 ············ 177
• 8.4.2AutoEncoder 类 ·················· 178
• 8.4.3应用于MNIST 数据集的基本自编码器 ···················· 180
• 8.4.4基本自编码器的完整代码 ··· 184
• 8.4.5基本自编码器小结 ············ 186
• 8.5加性高斯噪声自编码器 ················ 186
• 8.5.1自编码器类 ························ 187
• 8.5.2应用于MNIST 数据集的加性高斯自编码器 ············ 188
• 8.5.3绘制重建的图像 ················ 191
• 8.5.4加性高斯自编码器的完整代码 ······················· 192
• 8.5.5比较基本自编码器和加性高斯噪声自编码器 ············ 193
• 8.5.6加性高斯噪声自编码器小结 ······················· 194
• 8.6稀疏自编码器 ································ 194
• 8.6.1KL 散度 ······························ 194
• 8.6.2稀疏自编码器的完整代码 ························· 196
• 8.6.3应用于MNIST 数据集的稀疏自编码器 ···················· 198
• 8.6.4比较稀疏自编码器和加性高斯噪声自编码器 ············ 200
• 8.7总结 ···································· 200
• 第9 章神经网络研究 ························· 201
• 9.1神经网络中避免过拟合 ················ 201
• 9.1.1过拟合问题阐述 ················ 201
• 9.1.2过拟合解决方案 ················ 202
• 9.1.3影响效果 ···························· 203
• 9.2使用神经网络进行大规模视频处理 ······················ 204
• 9.2.1分辨率改进方案 ················ 204
• 9.2.2特征直方图基线 ················ 205
• 9.2.3定量结果 ···························· 205
• 9.3使用双分支互向神经网络进行命名实体识别 ································ 206
• 9.3.1命名实体识别的例子 ········ 206
• 9.3.2定义Twinet ························ 207
• 9.3.3结果 ···································· 208
• 9.4双向递归神经网络 ························ 208
• 9.5总结 ···································· 209
• 第10 章开始使用TensorFlow ········ 211
• 10.1环境搭建 ······································ 211
• 10.2比较TensorFlow 和Numpy ········ 212
• 10.3计算图 ·········································· 213
• 10.3.1图 ···································· 213
• 10.3.2会话对象 ························ 214
• 10.3.3变量 ································ 215
• 10.3.4域 ···································· 216
• 10.3.5数据输入 ························ 217
• 10.3.6占位符和输入字典 ········ 217
• 10.4自动微分 ·································· 218
• 10.5TensorBoard ································· 219