博客、教程收集

柔光的暖阳◎ 2022-05-18 06:47 156阅读 0赞

### 算法 ###

1.  [图的遍历 - 广度优先和深度优先算法][-]
2.  [二叉树怎样序列化才能重建][Link 1]
3.  [如何理解特征值和特征向量  
    ][Link 2]
4.  [压缩感知中MP和OMP算法][MP_OMP]
5.  [K-SVD算法][K-SVD]

### 深度学习 ###

1.  [Implementing a CNN for Text Classification in TensorFlow][]
2.  [Understanding Convolutional Neural Networks for NLP][]
3.  [RNNs in Tensorflow, a Practical Guide and Undocumented Features][RNNs in Tensorflow_ a Practical Guide and Undocumented Features]
4.  [Attention and Memory in Deep Learning and NLP][]
5.  [卷积神经网络CNN在自然语言处理中的应用][CNN]
6.  [大牛教程][Link 3]
7.  [ATTENTION MECHANISM][]
8.  [Text Classification, Part 3 - Hierarchical attention network][Text Classification_ Part 3 - Hierarchical attention network]
9.  [《Attention is All You Need》浅读][Attention is All You Need]

### RNN结构和实现 ###

1.  [LSTM神经网络输入输出究竟是怎样的？][LSTM]
2.  [双向循环神经网络及TensorFlow实现][TensorFlow]
3.  [Understanding LSTM Networks][]
4.  [The Unreasonable Effectiveness of Recurrent Neural Networks][]
5.  [Recurrent Neural Networks Tutorial, Part 1 – Introduction to RNNs][Recurrent Neural Networks Tutorial_ Part 1 _ Introduction to RNNs]
6.  [RNN加上Attention][RNN_Attention]
7.  [RNN 循环神经网络系列 4: 注意力机制][RNN_Attention]
8.  [Understanding LSTM and its diagrams][]

### 深度学习理论 ###

1.  [深度学习最全优化方法总结比较（SGD，Adagrad，Adadelta，Adam，Adamax，Nadam）][SGD_Adagrad_Adadelta_Adam_Adamax_Nadam]
2.  [Softmax函数与交叉熵][Softmax]
3.  [Soft & hard attention][Soft _ hard attention]
4.  [Deep learning - Computation & optimization.][Deep learning - Computation _ optimization.]
5.  [Deep learning - Linear algebra.][]
6.  [Attention? Attention!][Attention_ Attention]
7.  [The Transformer – Attention is all you need.][The Transformer _ Attention is all you need.]

### 前沿论文 ###

1.  [西土城的搬砖日常][Link 4]
2.  [语义相似度计算][Link 5]

### 课程 ###

1.  [Index of /class/nn4nlp2017/assets/slides][Index of _class_nn4nlp2017_assets_slides]

### 工具使用 ###

1.  [Markdown公式][Markdown]
2.  [Markdown公式2][Markdown_2]

### 教程 ###

1.  [大数据入门与实战-PySpark的使用教程  
    ][-PySpark_]

### Python ###

#### Numpy ####

1.  [如何理解和使用NumPy.einsum？][NumPy.einsum]

#### TensorFlow ####

1.  [tf.einsum][]
2.  [tensor-to-tensor\[理论篇\]][tensor-to-tensor]
3.  https://zhuanlan.zhihu.com/p/32870503

[-]: https://www.jianshu.com/p/5ab7d6a0fd17
[Link 1]: https://zhuanlan.zhihu.com/p/26418233
[Link 2]: https://blog.csdn.net/ljhandlwt/article/details/76576915
[MP_OMP]: https://www.cnblogs.com/AndyJee/p/5047174.html#top
[K-SVD]: https://www.cnblogs.com/endlesscoding/p/10090866.html
[Implementing a CNN for Text Classification in TensorFlow]: http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/
[Understanding Convolutional Neural Networks for NLP]: http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/
[RNNs in Tensorflow_ a Practical Guide and Undocumented Features]: http://www.wildml.com/2016/08/rnns-in-tensorflow-a-practical-guide-and-undocumented-features/
[Attention and Memory in Deep Learning and NLP]: http://www.wildml.com/2016/01/attention-and-memory-in-deep-learning-and-nlp/
[CNN]: https://www.cnblogs.com/yelbosh/p/5808706.html
[Link 3]: https://theneuralperspective.com/2016/11/26/convolutional-text-classification/
[ATTENTION MECHANISM]: https://blog.heuritech.com/2016/01/20/attention-mechanism/
[Text Classification_ Part 3 - Hierarchical attention network]: https://richliao.github.io/supervised/classification/2016/12/26/textclassifier-HATN/
[Attention is All You Need]: https://kexue.fm/archives/4765
[LSTM]: https://www.zhihu.com/question/41949741
[TensorFlow]: https://blog.csdn.net/wangyangzhizhou/article/details/79798087
[Understanding LSTM Networks]: http://colah.github.io/posts/2015-08-Understanding-LSTMs/
[The Unreasonable Effectiveness of Recurrent Neural Networks]: http://karpathy.github.io/2015/05/21/rnn-effectiveness/
[Recurrent Neural Networks Tutorial_ Part 1 _ Introduction to RNNs]: http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/
[RNN_Attention]: https://juejin.im/post/59f72f61f265da432002871c
[Understanding LSTM and its diagrams]: https://medium.com/mlreview/understanding-lstm-and-its-diagrams-37e2f46f1714
[SGD_Adagrad_Adadelta_Adam_Adamax_Nadam]: https://zhuanlan.zhihu.com/p/22252270
[Softmax]: https://zhuanlan.zhihu.com/p/27223959
[Soft _ hard attention]: https://jhui.github.io/2017/03/15/Soft-and-hard-attention/
[Deep learning - Computation _ optimization.]: https://jhui.github.io/2017/01/05/Deep-learning-computation-and-optimization/
[Deep learning - Linear algebra.]: https://jhui.github.io/2017/01/05/Deep-learning-linear-algebra/
[Attention_ Attention]: https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html
[The Transformer _ Attention is all you need.]: https://mchromiak.github.io/articles/2017/Sep/12/Transformer-Attention-is-all-you-need/#
[Link 4]: https://zhuanlan.zhihu.com/xitucheng10
[Link 5]: http://nlp.town/blog/sentence-similarity/
[Index of _class_nn4nlp2017_assets_slides]: http://phontron.com/class/nn4nlp2017/assets/slides/
[Markdown]: http://www.domuse.com/markdown-and-latex-equation-handbook.html
[Markdown_2]: https://www.zybuluo.com/codeep/note/163962
[-PySpark_]: https://www.jianshu.com/p/5a42fe0eed4d
[NumPy.einsum]: https://zhuanlan.zhihu.com/p/27739282
[tf.einsum]: https://www.tensorflow.org/api_docs/python/tf/einsum
[tensor-to-tensor]: https://daiwk.github.io/posts/platform-tensor-to-tensor.html