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Title: GitHub - zeroToAll/tensorflow_practice: tensorflow实战练习，包括强化学习、推荐系统、nlp等

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Description: tensorflow实战练习，包括强化学习、推荐系统、nlp等. Contribute to zeroToAll/tensorflow_practice development by creating an account on GitHub.

Open Graph Description: tensorflow实战练习，包括强化学习、推荐系统、nlp等. Contribute to zeroToAll/tensorflow_practice development by creating an account on GitHub.

X Description: tensorflow实战练习，包括强化学习、推荐系统、nlp等. Contribute to zeroToAll/tensorflow_practice development by creating an account on GitHub.

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https://patch-diff.githubusercontent.com/zeroToAll/tensorflow_practice#推荐阅读
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
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Chatbothttps://www.jianshu.com/p/aab40f439012
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 DQNhttps://www.jianshu.com/p/fae51b5fe000
DQN三大改进(二)-Prioritised replayhttps://www.jianshu.com/p/db14fdc67d2c
DQN三大改进(三)-Dueling Networkhttps://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
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ESSMhttps://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
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
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