# tensorflow_practice **Repository Path**: deeplearningrepos/tensorflow_practice ## Basic Information - **Project Name**: tensorflow_practice - **Description**: tensorflow实战练习，包括强化学习、推荐系统、nlp等 - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2021-03-30 - **Last Updated**: 2021-08-31 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README Tensroflow练习 ====== 相关数据集下载地址：链接:https://pan.baidu.com/s/1GMv7_3qruoVZBJMvN-afGA 密码:ako7 基于tf1.4 目录 1、基础
[基本语法
](https://github.com/princewen/tensorflow_practice/blob/master/basic/basic.py) [tensorBoard使用
](https://github.com/princewen/tensorflow_practice/blob/master/basic/tensorBoard.py) [dropout
](https://github.com/princewen/tensorflow_practice/blob/master/basic/dropout.py) [模型保存与重载
](https://github.com/princewen/tensorflow_practice/blob/master/basic/save2file.py) [基本神经网络
](https://github.com/princewen/tensorflow_practice/blob/master/basic/first_nerual_network.py) [卷积神经网络
](https://github.com/princewen/tensorflow_practice/blob/master/basic/CNN.py)
2、自然语言相关
[static_RNN
](https://github.com/princewen/tensorflow_practice/blob/master/nlp/RNN_static_cell.py) [dynamic_RNN
](https://github.com/princewen/tensorflow_practice/blob/master/nlp/RNN_dynamic_cell.py) [LSTM
](https://github.com/princewen/tensorflow_practice/blob/master/nlp/LSTM.py) [LSTM_regression
](https://github.com/princewen/tensorflow_practice/blob/master/nlp/LSTM_Regression.py) [seq2seq
](https://github.com/princewen/tensorflow_practice/blob/master/nlp/basic_seq2seq.py) [seq2seq_attention
](https://github.com/princewen/tensorflow_practice/tree/master/nlp/chat_bot_seq2seq_attention)
3、强化学习相关
[Q-learning
](https://github.com/princewen/tensorflow_practice/tree/master/RL/my_q_learning_new) [SARSA
](https://github.com/princewen/tensorflow_practice/tree/master/RL/SARSA) [SARSA-lambda
](https://github.com/princewen/tensorflow_practice/tree/master/RL/sarsa_lambda) [DQN
](https://github.com/princewen/tensorflow_practice/tree/master/RL/DQN-demo) [Double DQN
](https://github.com/princewen/tensorflow_practice/tree/master/RL/Double-DQN-demo) [Dueling DQN
](https://github.com/princewen/tensorflow_practice/tree/master/RL/Dueling%20DQN%20Demo) [Prioritized Replay DQN
](https://github.com/princewen/tensorflow_practice/tree/master/RL/Prioritized_Replay_DQN_demo) [Policy Gradient
](https://github.com/princewen/tensorflow_practice/tree/master/RL/Basic-Policy-Network) [Actor-Critic
](https://github.com/princewen/tensorflow_practice/tree/master/RL/Basic-Actor-Critic) [DDPG
](https://github.com/princewen/tensorflow_practice/tree/master/RL/Basic-DDPG) [Pointer-Network
](https://github.com/princewen/tensorflow_practice/tree/master/RL/myPtrNetwork) [MADDPG
](https://github.com/princewen/tensorflow_practice/tree/master/RL/Basic-MADDPG-Demo)
4、推荐系统
[FM
](https://github.com/princewen/tensorflow_practice/tree/master/recommendation/recommendation-FM-demo) [FFM
](https://github.com/princewen/tensorflow_practice/tree/master/recommendation/recommendation-FFM-Demo) [DeepFM
](https://github.com/princewen/tensorflow_practice/tree/master/recommendation/Basic-DeepFM-model) [Deep Cross Network
](https://github.com/princewen/tensorflow_practice/tree/master/recommendation/Basic-DCN-Demo) [P NN
](https://github.com/princewen/tensorflow_practice/tree/master/recommendation/Basic-PNN-Demo) [NFM
](https://github.com/princewen/tensorflow_practice/tree/master/recommendation/Basic-NFM-Demo) [AFM
](https://github.com/princewen/tensorflow_practice/tree/master/recommendation/Basic-AFM-Demo) [MLR
](https://github.com/princewen/tensorflow_practice/tree/master/recommendation/Basic-MLR-Demo) [DIN
](https://github.com/princewen/tensorflow_practice/tree/master/recommendation/Basic-DIN-Demo) [Bandit
](https://github.com/princewen/tensorflow_practice/tree/master/recommendation/Basic-Bandit-Demo) [GBDT+LR
](https://github.com/princewen/tensorflow_practice/tree/master/recommendation/GBDT%2BLR-Demo) [evaluation-metrics
](https://github.com/princewen/tensorflow_practice/tree/master/recommendation/Basic-Evaluation-metrics) [NCF
](https://github.com/princewen/tensorflow_practice/tree/master/recommendation/Basic-NCF-Demo)
5、GAN
[Basic GAN
](https://github.com/princewen/tensorflow_practice/blob/master/GAN/GAN.py) [SeqGAN
](https://github.com/princewen/tensorflow_practice/tree/master/GAN/seqgan)


推荐阅读 ============== 1、基础
[TensorFlow基础知识点总结
](https://www.jianshu.com/p/ce213e6b2dc0) [用tensorboard来看看我们的网络流吧
](https://www.jianshu.com/p/41466470b347) [使用dropout来避免过拟合吧
](https://www.jianshu.com/p/4f1b525ddf86) [使用Tensorflow实现第一个神经网络吧！
](https://www.jianshu.com/p/596a30d46f34y) [实现CNN对mnist手写数字分类
](https://www.jianshu.com/p/49ab6568e472)
2、自然语言相关
[使用简单的RNN观测数字中的规律
](https://www.jianshu.com/p/3ccc1eb5fda2) [更进一步，使用LSTM实现对手写数字识别
](https://www.jianshu.com/p/d25baccde6bc) [简单的Seq2Seq实现作对联
](https://www.jianshu.com/p/83443b2baf27) [使用Seq2Seq+attention model实现简单的Chatbot
](https://www.jianshu.com/p/aab40f439012)
3、强化学习相关
[实战深度强化学习DQN-理论和实践
](https://www.jianshu.com/p/10930c371cac) [DQN三大改进(一)-Double DQN
](https://www.jianshu.com/p/fae51b5fe000) [DQN三大改进(二)-Prioritised replay
](https://www.jianshu.com/p/db14fdc67d2c) [DQN三大改进(三)-Dueling Network
](https://www.jianshu.com/p/b421c85796a2) [深度强化学习-Policy Gradient基本实现
](https://www.jianshu.com/p/2ccbab48414b) [深度强化学习-Actor-Critic算法原理和实现
](https://www.jianshu.com/p/6fe18d0d8822) [深度强化学习-DDPG算法原理和实现
](https://www.jianshu.com/p/6fe18d0d8822) [Pointer-network理论及tensorflow实战
](https://www.jianshu.com/p/2ad389e91467) [探秘多智能体强化学习-MADDPG算法原理及简单实现
](https://www.jianshu.com/p/4e4e35d80137)
4、推荐系统
[推荐系统遇上深度学习(一)--FM模型理论和实践
](https://www.jianshu.com/p/152ae633fb00) [推荐系统遇上深度学习(二)--FFM模型理论和实践
](https://www.jianshu.com/p/781cde3d5f3d) [推荐系统遇上深度学习(三)--DeepFM模型理论和实践
](https://www.jianshu.com/p/6f1c2643d31b) [推荐系统遇上深度学习(四)--多值离散特征的embedding解决方案
](https://www.jianshu.com/p/4a7525c018b2) [推荐系统遇上深度学习(五)--Deep&Cross Network模型理论和实践
](https://www.jianshu.com/p/77719fc252fa) [推荐系统遇上深度学习(六)--PNN模型理论和实践
](https://www.jianshu.com/p/be784ab4abc2) [推荐系统遇上深度学习(七)--NFM模型理论和实践
](https://www.jianshu.com/p/4e65723ee632) [推荐系统遇上深度学习(八)--AFM模型理论和实践
](https://www.jianshu.com/p/83d3b2a1e55d) [推荐系统遇上深度学习(九)--评价指标AUC原理及实践
](https://www.jianshu.com/p/4dde15a56d44) [推荐系统遇上深度学习(十)--GBDT+LR融合方案实战
](https://www.jianshu.com/p/96173f2c2fb4) [推荐系统遇上深度学习(十一)--神经协同过滤NCF原理及实战
](https://www.jianshu.com/p/6173dbde4f53) [推荐系统遇上深度学习(十二)--推荐系统中的EE问题及基本Bandit算法
](https://www.jianshu.com/p/95b2de50ce44) [推荐系统遇上深度学习(十三)--linUCB方法浅析及实现
](https://www.jianshu.com/p/e0e843d78e3c) [推荐系统遇上深度学习(十四)--《DRN:A Deep Reinforcement Learning Framework for News Recommendation》
](https://www.jianshu.com/p/c0384b213320) [推荐系统遇上深度学习(十五)--强化学习在京东推荐中的探索
](https://www.jianshu.com/p/b9113332e33e) [推荐系统遇上深度学习(十六)--详解推荐系统中的常用评测指标
](https://www.jianshu.com/p/665f9f168eff) [推荐系统遇上深度学习(十七)--探秘阿里之MLR算法浅析及实现
](https://www.jianshu.com/p/627fc0d755b2) [推荐系统遇上深度学习(十八)--探秘阿里之深度兴趣网络(DIN)浅析及实现
](https://www.jianshu.com/p/73b6f5d00f46) [推荐系统遇上深度学习(十九)--探秘阿里之完整空间多任务模型ESSM
](https://www.jianshu.com/p/35f00299c059) [推荐系统遇上深度学习(二十)--贝叶斯个性化排序(BPR)算法原理及实战
](https://www.jianshu.com/p/ba1936ee0b69) [推荐系统遇上深度学习(二十一)--阶段性回顾
](https://www.jianshu.com/p/99e8f24ec7df) [推荐系统遇上深度学习(二十二)--DeepFM升级版XDeepFM模型强势来袭！
](https://www.jianshu.com/p/b4128bc79df0) [推荐系统遇上深度学习(二十三)--大一统信息检索模型IRGAN在推荐领域的应用
](https://www.jianshu.com/p/d151b52e57f9) [推荐系统遇上深度学习(二十四)--深度兴趣进化网络DIEN原理及实战！
](https://www.jianshu.com/p/6742d10b89a8) [推荐系统遇上深度学习(二十五)--当知识图谱遇上个性化推荐
](https://www.jianshu.com/p/6a5e796499e8) [推荐系统遇上深度学习(二十六)--知识图谱与推荐系统结合之DKN模型原理及实现
](https://www.jianshu.com/p/2e3cade31098) [推荐系统遇上深度学习(二十七)--知识图谱与推荐系统结合之RippleNet模型原理及实现
](https://www.jianshu.com/p/c5ffaf7ed449)
5、GAN
[听说GAN很高大上，其实就这么简单
](https://www.jianshu.com/p/5f638f493b7a) [对抗思想与强化学习的碰撞-SeqGAN模型原理和代码解析
](https://www.jianshu.com/p/de4e913e0580)