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精通 TensorFlow 1.x 中文版(初稿)

2019-09-25 10:15 作者:绝不原创的飞龙  | 我要投稿
  • TensorFlow 101(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/8.md)

  • 什么是 TensorFlow?(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/9.md)

  • TensorFlow 核心(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/10.md)

  • 代码预热 - Hello TensorFlow(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/11.md)

  • 张量(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/12.md)

  • 常量(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/13.md)

  • 操作(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/14.md)

  • 占位符(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/15.md)

  • 从 Python 对象创建张量(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/16.md)

  • 变量(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/17.md)

  • 从库函数生成的张量(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/18.md)

  • 使用相同的值填充张量元素(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/19.md)

  • 用序列填充张量元素(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/20.md)

  • 使用随机分布填充张量元素(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/21.md)

  • 使用tf.get_variable()获取变量(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/22.md)

  • 数据流图或计算图(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/23.md)

  • 执行顺序和延迟加载(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/24.md)

  • 跨计算设备执行图 - CPU 和 GPU(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/25.md)

  • 将图节点放置在特定的计算设备上(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/26.md)

  • 简单放置(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/27.md)

  • 动态展示位置(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/28.md)

  • 软放置(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/29.md)

  • GPU 内存处理(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/30.md)

  • 多个图(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/31.md)

  • TensorBoard(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/32.md)

  • TensorBoard 最小的例子(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/33.md)

  • TensorBoard 详情(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/34.md)

  • 总结(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/35.md)

  • TensorFlow 的高级库(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/36.md)

  • TF Estimator - 以前的 TF 学习(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/37.md)

  • TF Slim(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/38.md)

  • TFLearn(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/39.md)

  • 创建 TFLearn 层(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/40.md)

  • TFLearn 核心层(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/41.md)

  • TFLearn 卷积层(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/42.md)

  • TFLearn 循环层(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/43.md)

  • TFLearn 正则化层(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/44.md)

  • TFLearn 嵌入层(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/45.md)

  • TFLearn 合并层(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/46.md)

  • TFLearn 估计层(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/47.md)

  • 创建 TFLearn 模型(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/48.md)

  • TFLearn 模型的类型(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/49.md)

  • 训练 TFLearn 模型(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/50.md)

  • 使用 TFLearn 模型(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/51.md)

  • PrettyTensor(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/52.md)

  • Sonnet(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/53.md)

  • 总结(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/54.md)

  • Keras 101(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/55.md)

  • 安装 Keras(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/56.md)

  • Keras 中的神经网络模型(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/57.md)

  • 在 Keras 建立模型的工作流程(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/58.md)

  • 创建 Keras 模型(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/59.md)

  • 用于创建 Keras 模型的顺序 API(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/60.md)

  • 用于创建 Keras 模型的函数式 API(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/61.md)

  • Keras 层(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/62.md)

  • Keras 核心层(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/63.md)

  • Keras 卷积层(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/64.md)

  • Keras 池化层(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/65.md)

  • Keras 本地连接层(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/66.md)

  • Keras 循环层(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/67.md)

  • Keras 嵌入层(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/68.md)

  • Keras 合并层(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/69.md)

  • Keras 高级激活层(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/70.md)

  • Keras 正则化层(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/71.md)

  • Keras 噪音层(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/72.md)

  • 将层添加到 Keras 模型(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/73.md)

  • 用于将层添加到 Keras 模型的顺序 API(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/74.md)

  • 用于向 Keras 模型添加层的函数式 API(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/75.md)

  • 编译 Keras 模型(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/76.md)

  • 训练 Keras 模型(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/77.md)

  • 使用 Keras 模型进行预测(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/78.md)

  • Keras 的附加模块(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/79.md)

  • MNIST 数据集的 Keras 序列模型示例(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/80.md)

  • 总结(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/81.md)

  • 使用 TensorFlow 进行经典机器学习(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/82.md)

  • 简单的线性回归(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/83.md)

  • 数据准备(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/84.md)

  • 构建一个简单的回归模型(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/85.md)

  • 定义输入,参数和其他变量(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/86.md)

  • 定义模型(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/87.md)

  • 定义损失函数(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/88.md)

  • 定义优化器函数(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/89.md)

  • 训练模型(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/90.md)

  • 使用训练的模型进行预测(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/91.md)

  • 多元回归(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/92.md)

  • 正则化回归(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/93.md)

  • 套索正则化(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/94.md)

  • 岭正则化(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/95.md)

  • ElasticNet 正则化(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/96.md)

  • 使用逻辑回归进行分类(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/97.md)

  • 二分类的逻辑回归(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/98.md)

  • 多类分类的逻辑回归(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/99.md)

  • 二分类(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/100.md)

  • 多类分类(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/101.md)

  • 总结(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/102.md)

  • 使用 TensorFlow 和 Keras 的神经网络和 MLP(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/103.md)

  • 感知机(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/104.md)

  • 多层感知机(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/105.md)

  • 用于图像分类的 MLP(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/106.md)

  • 用于 MNIST 分类的基于 TensorFlow 的 MLP(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/107.md)

  • 用于 MNIST 分类的基于 Keras 的 MLP(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/108.md)

  • 用于 MNIST 分类的基于 TFLearn 的 MLP(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/109.md)

  • 使用 TensorFlow,Keras 和 TFLearn 的 MLP 总结(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/110.md)

  • 用于时间序列回归的 MLP(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/111.md)

  • 总结(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/112.md)

  • 使用 TensorFlow 和 Keras 的 RNN(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/113.md)

  • 简单循环神经网络(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/114.md)

  • RNN 变种(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/115.md)

  • LSTM 网络(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/116.md)

  • GRU 网络(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/117.md)

  • TensorFlow RNN(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/118.md)

  • TensorFlow RNN 单元类(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/119.md)

  • TensorFlow RNN 模型构建类(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/120.md)

  • TensorFlow RNN 单元包装器类(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/121.md)

  • 适用于 RNN 的 Keras(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/122.md)

  • RNN 的应用领域(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/123.md)

  • 用于 MNIST 数据的 Keras 中的 RNN(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/124.md)

  • 总结(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/125.md)

  • 使用 TensorFlow 和 Keras 的时间序列数据的 RNN(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/126.md)

  • 航空公司乘客数据集(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/127.md)

  • 加载 airpass 数据集(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/128.md)

  • 可视化 airpass 数据集(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/129.md)

  • 使用 TensorFlow RNN 模型预处理数据集(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/130.md)

  • TensorFlow 中的简单 RNN(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/131.md)

  • TensorFlow 中的 LSTM(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/132.md)

  • TensorFlow 中的 GRU(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/133.md)

  • 使用 Keras RNN 模型预处理数据集(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/134.md)

  • 使用 Keras 的简单 RNN(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/135.md)

  • 使用 Keras 的 LSTM(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/136.md)

  • 使用 Keras 的 GRU(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/137.md)

  • 总结(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/138.md)

  • 使用 TensorFlow 和 Keras 的文本数据的 RNN(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/139.md)

  • 词向量表示(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/140.md)

  • 为 word2vec 模型准备数据(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/141.md)

  • 加载和准备 PTB 数据集(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/142.md)

  • 加载和准备 text8 数据集(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/143.md)

  • 准备小验证集(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/144.md)

  • 使用 TensorFlow 的 skip-gram 模型(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/145.md)

  • 使用 t-SNE 可视化单词嵌入(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/146.md)

  • keras 的 skip-gram 模型(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/147.md)

  • 使用 TensorFlow 和 Keras 中的 RNN 模型生成文本(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/148.md)

  • TensorFlow 中的 LSTM 文本生成(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/149.md)

  • Keras 中的 LSTM 文本生成(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/150.md)

  • 总结(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/151.md)

  • 使用 TensorFlow 和 Keras 的 CNN(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/152.md)

  • 理解卷积(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/153.md)

  • 了解池化(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/154.md)

  • CNN 架构模式 - LeNet(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/155.md)

  • 用于 MNIST 数据的 LeNet(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/156.md)

  • 使用 TensorFlow 的用于 MNIST 的 LeNet CNN(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/157.md)

  • 使用 Keras 的用于 MNIST 的 LeNet CNN(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/158.md)

  • 用于 CIFAR10 数据的 LeNet(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/159.md)

  • 使用 TensorFlow 的用于 CIFAR10 的 ConvNets(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/160.md)

  • 使用 Keras 的用于 CIFAR10 的 ConvNets(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/161.md)

  • 总结(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/162.md)

  • 使用 TensorFlow 和 Keras 的自编码器(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/163.md)

  • 自编码器类型(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/164.md)

  • TensorFlow 中的栈式自编码器(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/165.md)

  • Keras 中的栈式自编码器(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/166.md)

  • TensorFlow 中的去噪自编码器(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/167.md)

  • Keras 中的去噪自编码器(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/168.md)

  • TensorFlow 中的变分自编码器(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/169.md)

  • Keras 中的变分自编码器(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/170.md)

  • 总结(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/171.md)

  • TF 服务:生产中的 TensorFlow 模型(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/172.md)

  • 在 TensorFlow 中保存和恢复模型(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/173.md)

  • 使用保护程序类保存和恢复所有图变量(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/174.md)

  • 使用保护程序类保存和恢复所选变量(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/175.md)

  • 保存和恢复 Keras 模型(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/176.md)

  • TensorFlow 服务(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/177.md)

  • 安装 TF 服务(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/178.md)

  • 保存 TF 服务的模型(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/179.md)

  • 提供 TF 服务模型(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/180.md)

  • 在 Docker 容器中提供 TF 服务(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/181.md)

  • 安装 Docker(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/182.md)

  • 为 TF 服务构建 Docker 镜像(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/183.md)

  • 在 Docker 容器中提供模型(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/184.md)

  • Kubernetes 中的 TensorFlow 服务(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/185.md)

  • 安装 Kubernetes(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/186.md)

  • 将 Docker 镜像上传到 dockerhub(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/187.md)

  • 在 Kubernetes 部署(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/188.md)

  • 总结(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/189.md)

  • 迁移学习和预训练模型(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/190.md)

  • ImageNet 数据集(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/191.md)

  • 再训练或微调模型(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/192.md)

  • COCO 动物数据集和预处理图像(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/193.md)

  • TensorFlow 中的 VGG16(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/194.md)

  • 使用 TensorFlow 中预训练的 VGG16 进行图像分类(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/195.md)

  • TensorFlow 中的图像预处理,用于预训练的 VGG16(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/196.md)

  • 使用 TensorFlow 中的再训练的 VGG16 进行图像分类(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/197.md)

  • Keras 的 VGG16(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/198.md)

  • 使用 Keras 中预训练的 VGG16 进行图像分类(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/199.md)

  • 使用 Keras 中再训练的 VGG16 进行图像分类(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/200.md)

  • TensorFlow 中的 Inception v3(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/201.md)

  • 使用 TensorFlow 中的 Inception v3 进行图像分类(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/202.md)

  • 使用 TensorFlow 中的再训练的 Inception v3 进行图像分类(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/203.md)

  • 总结(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/204.md)

  • 深度强化学习(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/205.md)

  • OpenAI Gym 101(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/206.md)

  • 将简单的策略应用于 cartpole 游戏(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/207.md)

  • 强化学习 101(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/208.md)

  • Q 函数(在模型不可用时学习优化)(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/209.md)

  • RL 算法的探索与开发(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/210.md)

  • V 函数(模型可用时学习优化)(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/211.md)

  • 强化学习技巧(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/212.md)

  • 强化学习的朴素神经网络策略(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/213.md)

  • 实现 Q-Learning(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/214.md)

  • Q-Learning 的初始化和离散化(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/215.md)

  • 使用 Q-Table 进行 Q-Learning(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/216.md)

  • Q-Network 或深 Q 网络(DQN)的 Q-Learning(https://gitee.com/it-ebooks/dsai-trans-proj/blob/master/mastering-tf-1x-zh/217.md)



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