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1 | 1 | # Topics in Brief
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2 | 2 |
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3 |
| -Set of notebooks associated with Chapter 7 of the book. |
| 3 | +## 🔖 Outline |
4 | 4 |
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| 5 | +To be added |
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| 7 | + |
| 8 | +## 🗒️ Notebooks |
| 9 | + |
| 10 | +Set of notebooks associated with the chapter. |
5 | 11 |
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6 | 12 | 1. **[Elastic Search](https://github.com/practical-nlp/practical-nlp/blob/master/Ch7/01_ElasticSearch.ipynb)**: Here we demonstrate on the CMU book summaries dataset how to use Elastic Search to index and search through data.
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7 | 13 |
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| -2. **[Topic Modelling](https://github.com/practical-nlp/practical-nlp/blob/master/Ch7/02_TopicModelling.ipynb)**: Here we demonstrate common Topic Modelling techniques such as LDA,LSA using gensim. |
| 14 | +2. **[Topic Modelling](https://github.com/practical-nlp/practical-nlp/blob/master/Ch7/02_TopicModelling.ipynb)**: Here we demonstrate common Topic Modelling techniques such as LDA, LSA using gensim. |
9 | 15 |
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10 | 16 | 3. **[Text Summarization](http://localhost:8888/notebooks/Ch7/03_TextSummarization.ipynb)**: Here we demonstrate extractive and abstractive text summarization using various packages such as gensim, summy, etc and pre-trained models such as BERT.
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11 | 17 |
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12 | 18 | 4. **[Recommender Systems](https://github.com/practical-nlp/practical-nlp/blob/master/Ch7/04_RecommenderSystems.ipynb)**: Here we demonstrate a simple Recommender System using Doc2Vec.
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13 | 19 |
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| -5. **[Machine Translation](https://github.com/practical-nlp/practical-nlp/blob/master/Ch7/05_MachineTranslation.ipynb)**: Here we demonstrate using the Machine Translation service provided by Microsoft's Azure Cognitive services API. |
| 20 | +5. **[Machine Translation](https://github.com/practical-nlp/practical-nlp/blob/master/Ch7/05_MachineTranslation.ipynb)**: Here we demonstrate using the Machine Translation service provided by Microsoft's Azure Cognitive Services API. |
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| 23 | +## 🖼️ Figures |
| 24 | + |
| 25 | +Color figures as requested by the readers. |
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