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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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