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1 | 1 |
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2 | 2 | # [Chapter 7. Learning Text Representations](#) |
3 | | - |
4 | | -* [7.1. Understanding Word2vec Model](https://github.com/sudharsan13296/Hands-On-Deep-Learning-Algorithms-with-Python/blob/master/07.%20Learning%20Text%20Representations/7.01%20Understanding%20Word2vec%20Model.ipynb) |
5 | | -* 7.2. Continuous Bag of words |
6 | | -* 7.3. Math of CBOW |
7 | | - * 7.3.1. Deriving Forward Propagation |
8 | | - * 7.3.2. Deriving Backward Propagation |
9 | | -* 7.4. Skip- Gram model |
10 | | -* 7.5. Math of Skip-Gram |
11 | | - * 7.5.1. Deriving Forward Propagation |
12 | | - * 7.5.2. Deriving Backward Propagation |
13 | | -* 7.6. various training strategies |
14 | | - * 7.6.1. Hierarchical Softmax |
15 | | - * 7.6.2. Negative sampling |
16 | | - * 7.6.3. Subsampling frequent words |
17 | | -* [ 7.7. Building word2vec model using Gensim](https://github.com/sudharsan13296/Hands-On-Deep-Learning-Algorithms-with-Python/blob/master/07.%20Learning%20Text%20Representations/7.07%20Building%20word2vec%20model%20using%20Gensim.ipynb) |
18 | | -* [7.8. Visualizing word embeddings in TensorBoard](https://github.com/sudharsan13296/Hands-On-Deep-Learning-Algorithms-with-Python/blob/master/07.%20Learning%20Text%20Representations/7.08%20Visualizing%20Word%20Embeddings%20in%20TensorBoard.ipynb) |
19 | | -* 7.9. Converting documents to vectors using doc2vec |
20 | | - * 7.9.1. PV-DM |
21 | | - * 7.9.2. PV-DBOW |
22 | | -* [7.10. Finding similar documents using Doc2vec](https://github.com/sudharsan13296/Hands-On-Deep-Learning-Algorithms-with-Python/blob/master/07.%20Learning%20Text%20Representations/7.10%20Finding%20similar%20documents%20using%20Doc2Vec.ipynb) |
23 | | -* 7.11. Understanding skip thoughts algorithm |
24 | | -* 7.12 Quick thoughts for sentence embeddings |
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