Deep_Learning_TensorFlow2_Examples

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Deep_Learning_TensorFlow2_Examples

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深度学习TensorFlow2入门指南。Deep Learning TensorFlow2 API and Examples with Python3 and Jupyter Notebook.

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# 深度学习入门指南
基于TensorFlow2 + Keras讲解深度学习入门指南。

## 注意
如果通过Github站内超链接打开Jupyter Notebook文件发生错误，可以点击根据 https://nbviewer.org 生成的“备用链接”间接访问对应文件。  
或者通过以下链接访问整个项目的站外备用链接，注意点击站外备用链接里的非Jupyter Notebook格式文件会跳转回到Github仓库内：  
●  [**Deep_Learning_TensorFlow2_Examples**](https://nbviewer.org/github/solidglue/Deep_Learning_TensorFlow2_Examples/tree/master/)

## 张量 
●  [**张量**](https://github.com/solidglue/TensorFlow2_Keras_Guide_API_Jupyter_Demo/blob/master/01_TensorFlow_basics/01_01_Tensors.ipynb)       [~~*(备用链接)*~~](https://nbviewer.org/github/solidglue/Deep_Learning_TensorFlow2_Examples/blob/master/01_TensorFlow_basics/01_01_Tensors.ipynb)]  
●  [**变量**](https://github.com/solidglue/TensorFlow2_Keras_Guide_API_Jupyter_Demo/blob/master/01_TensorFlow_basics/01_02_Variables.ipynb)       [~~*(备用链接)*~~](https://nbviewer.org/github/solidglue/Deep_Learning_TensorFlow2_Examples/blob/master/01_TensorFlow_basics/01_02_Variables.ipynb)]     
●  [**自动微分**](https://github.com/solidglue/TensorFlow2_Keras_Guide_API_Jupyter_Demo/blob/master/01_TensorFlow_basics/01_03_Automatic_differentiation.ipynb)       [~~*(备用链接)*~~](https://nbviewer.org/github/solidglue/Deep_Learning_TensorFlow2_Examples/blob/master/01_TensorFlow_basics/01_03_Automatic_differentiation.ipynb)]    
●  图和函数简介  
●  [**模块、层和模型简介**](https://github.com/solidglue/TensorFlow2_Keras_Guide_API_Jupyter_Demo/blob/master/01_TensorFlow_basics/01_05_Modules_layers_and_models.ipynb)       [~~*(备用链接)*~~](https://nbviewer.org/github/solidglue/Deep_Learning_TensorFlow2_Examples/blob/master/01_TensorFlow_basics/01_05_Modules_layers_and_models.ipynb)]    
●  [**训练循环**](https://github.com/solidglue/TensorFlow2_Keras_Guide_API_Jupyter_Demo/blob/master/01_TensorFlow_basics/01_06_Training_loops.ipynb)       [~~*(备用链接)*~~](https://nbviewer.org/github/solidglue/Deep_Learning_TensorFlow2_Examples/blob/master/01_TensorFlow_basics/01_06_Training_loops.ipynb)]    
●  高级自动微分  
●  不规则张量  
●  [**稀疏张量**](https://github.com/solidglue/TensorFlow2_Keras_Guide_API_Jupyter_Demo/blob/master/04_TensorFlow_in_depth/04_04_Sparse_tensor.ipynb)       [~~*(备用链接)*~~](https://nbviewer.org/github/solidglue/Deep_Learning_TensorFlow2_Examples/blob/master/04_TensorFlow_in_depth/04_04_Sparse_tensor.ipynb)]    
●  Numpy API  
●  Tensor切片

## Keras
●  [**Sequential模型**](https://github.com/solidglue/TensorFlow2_Keras_Guide_API_Jupyter_Demo/blob/master/02_Keras/02_01_The_sequential_model.ipynb)       [~~*(备用链接)*~~](https://nbviewer.org/github/solidglue/Deep_Learning_TensorFlow2_Examples/blob/master/02_Keras/02_01_The_sequential_model.ipynb)]    
●  [**Functional API**](https://github.com/solidglue/TensorFlow2_Keras_Guide_API_Jupyter_Demo/blob/master/02_Keras/02_02_The_functional_API.ipynb)       [~~*(备用链接)*~~](https://nbviewer.org/github/solidglue/Deep_Learning_TensorFlow2_Examples/blob/master/02_Keras/02_02_The_functional_API.ipynb)]    
●  [**使用内置方法进行训练和评估**](https://github.com/solidglue/TensorFlow2_Keras_Guide_API_Jupyter_Demo/blob/master/02_Keras/02_03_Training_evaluation_with_the_built_in_methods.ipynb)       [~~*(备用链接)*~~](https://nbviewer.org/github/solidglue/Deep_Learning_TensorFlow2_Examples/blob/master/02_Keras/02_03_Training_evaluation_with_the_built_in_methods.ipynb)]    
●  通过子类化构建新层和模型  
●  [**保存并加载Keras模型**](https://github.com/solidglue/TensorFlow2_Keras_Guide_API_Jupyter_Demo/blob/master/02_Keras/02_05_Serialization_and_saving.ipynb)       [~~*(备用链接)*~~](https://nbviewer.org/github/solidglue/Deep_Learning_TensorFlow2_Examples/blob/master/02_Keras/02_05_Serialization_and_saving.ipynb)]    
●  [**使用预处理层**](https://github.com/solidglue/TensorFlow2_Keras_Guide_API_Jupyter_Demo/blob/master/02_Keras/02_07_Working_with_preprocessing_layers.ipynb)       [~~*(备用链接)*~~](https://nbviewer.org/github/solidglue/Deep_Learning_TensorFlow2_Examples/blob/master/02_Keras/02_07_Working_with_preprocessing_layers.ipynb)]    
●  [**自定义Model.fit的操作流程**](https://github.com/solidglue/TensorFlow2_Keras_Guide_API_Jupyter_Demo/blob/master/02_Keras/02_08_Customizing_what_happens_in_fit.ipynb)       [~~*(备用链接)*~~](https://nbviewer.org/github/solidglue/Deep_Learning_TensorFlow2_Examples/blob/master/02_Keras/02_08_Customizing_what_happens_in_fit.ipynb)]    
●  [**从头开始编写训练循环**](https://github.com/solidglue/TensorFlow2_Keras_Guide_API_Jupyter_Demo/blob/master/02_Keras/02_09_Writing_a_training_loop_from_scratch.ipynb)       [~~*(备用链接)*~~](https://nbviewer.org/github/solidglue/Deep_Learning_TensorFlow2_Examples/blob/master/02_Keras/02_09_Writing_a_training_loop_from_scratch.ipynb)]    
●  采用Keras的循环神经网络（RNN）  
●  [**采用Keras进行遮盖和填充**](https://github.com/solidglue/TensorFlow2_Keras_Guide_API_Jupyter_Demo/blob/master/02_Keras/02_11_Understading_masking_and_padding.ipynb)       [~~*(备用链接)*~~](https://nbviewer.org/github/solidglue/Deep_Learning_TensorFlow2_Examples/blob/master/02_Keras/02_11_Understading_masking_and_padding.ipynb)]    
●  自动编写回调  
●  迁移学习和微调  
●  使用TensorFlow Cloud训练Keras模型

## TensorFlow Core
●  [**TensorFlow Core API 快速入门**](https://github.com/solidglue/TensorFlow2_Keras_Guide_API_Jupyter_Demo/blob/master/03_Build_with_Core/03_01_Quickstart_for_core.ipynb)       [~~*(备用链接)*~~](https://nbviewer.org/github/solidglue/Deep_Learning_TensorFlow2_Examples/blob/master/03_Build_with_Core/03_01_Quickstart_for_core.ipynb)]    
●  [**使用 Core API 进行二元分类的逻辑回归**](https://github.com/solidglue/TensorFlow2_Keras_Guide_API_Jupyter_Demo/blob/master/03_Build_with_Core/03_02_Logistic_regression.ipynb)       [~~*(备用链接)*~~](https://nbviewer.org/github/solidglue/Deep_Learning_TensorFlow2_Examples/blob/master/03_Build_with_Core/03_02_Logistic_regression.ipynb)]

## 自定义
●  创建操作  
●  生成随机数字

## 数据输入流水线
●  [**tf.data**](https://github.com/solidglue/TensorFlow2_Keras_Guide_API_Jupyter_Demo/blob/master/06_Data_input_pipelines/06_01_tfdata.ipynb)       [~~*(备用链接)*~~](https://nbviewer.org/github/solidglue/Deep_Learning_TensorFlow2_Examples/blob/master/06_Data_input_pipelines/06_01_tfdata.ipynb)]    
●  优化流水线性能  
●  分析流水线性能

## 保存模型
●  [**Checkpoint**](https://github.com/solidglue/TensorFlow2_Keras_Guide_API_Jupyter_Demo/blob/master/07_Import_and_export/07_01_Checkpoint.ipynb)       [~~*(备用链接)*~~](https://nbviewer.org/github/solidglue/Deep_Learning_TensorFlow2_Examples/blob/master/07_Import_and_export/07_01_Checkpoint.ipynb)]    
●  [**SavedModel**](https://github.com/solidglue/Deep_Learning_TensorFlow2_Examples/blob/master/07_Import_and_export/07_02_SaveModel.ipynb)       [~~*(备用链接)*~~](https://nbviewer.org/github/solidglue/Deep_Learning_TensorFlow2_Examples/blob/master/07_Import_and_export/07_02_SaveModel.ipynb)]

## 加速器
●  分布式训练  
●  GPU  
●  TPU

## 性能
●  使用tf.function提升性能  
●  分析TensorFlow的性能  
●  优化GPU性能  
●  图优化  
●  混合精度

## TensorFlow Serving（服务）
●  TensorFlow Serving和Docker  
●  安装  
●  提供TensorFlow模型  
●  高级模型服务器配置

## *扩展

1.**推荐系统**  
王树森推荐系统公开课 - 基于小红书的场景讲解工业界真实的推荐系统。  
●  [**Recommender_System**](https://github.com/solidglue/Recommender_System)

2.**YouTuBe推荐系统排序模型**  
以"DNN_for_YouTube_Recommendations"模型和电影评分数据集（ml-1m）为基础，详尽的展示了如何基于TensorFlow2实现推荐系统排序模型。  
●  [**YouTube深度排序模型(多值embedding、多目标学习)**](https://github.com/solidglue/DNN_for_YouTube_Recommendations)

3.**推荐系统推理服务**  
基于Goalng、Docker和微服务思想实现了高并发、高性能和高可用的推荐系统推理微服务，包括多种召回/排序服务，并提供多种接口访问方式（REST、gRPC和Dubbo）等，每日可处理上千万次推理请求。   
● [**推荐系统推理微服务Golang**](https://github.com/solidglue/Recommender_System_Inference_Services)

4.**机器学习 Sklearn入门教程**  
●  [**机器学习Sklearn入门教程**](https://github.com/solidglue/Machine_Learning_Sklearn_Examples)

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