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Updating figures & descriptions for Ch 3 #23 
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‎Ch3/README.md

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# Text Representation
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Set of notebooks associated with Chapter 3 of the book.
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## 🔖 Outline
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1. **[One-Hot Encoding](https://github.com/practical-nlp/practical-nlp/blob/master/Ch3/01_OneHotEncoding.ipynb)**: Here we demonstrate One-Hot encoding from first principle as well as scikit learn's implementation on our toy corpus.
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2. **[Bag of Words](https://github.com/practical-nlp/practical-nlp/blob/master/Ch3/02_Bag_of_Words.ipynb)** : Here we demostrate how to arrive at the bag of words representation for our toy corpus.
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3. **[Bag of N Grams](https://github.com/practical-nlp/practical-nlp/blob/master/Ch3/03_Bag_of_N_Grams.ipynb)**: Here we demonstrate how Bag of N Grams work using our toy corpus.
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## 🗒️ Notebooks
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Set of notebooks associated with the chapter.
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1. **[One-Hot Encoding](https://github.com/practical-nlp/practical-nlp/blob/master/Ch3/01_OneHotEncoding.ipynb)**: Here we demonstrate One-Hot encoding from the first principle as well as scikit learn's implementation on our toy corpus.
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2. **[Bag of Words](https://github.com/practical-nlp/practical-nlp/blob/master/Ch3/02_Bag_of_Words.ipynb)** : Here we demonstrate how to arrive at the bag of words representation for our toy corpus.
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3. **[Bag of N Grams](https://github.com/practical-nlp/practical-nlp/blob/master/Ch3/03_Bag_of_N_Grams.ipynb)**: Here we demonstrate how Bag of N-Grams work using our toy corpus.
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4. **[TF-IDF](https://github.com/practical-nlp/practical-nlp/blob/master/Ch3/04_TF_IDF.ipynb)**: Here we demonstrate how to obtain the get the TF-IDF representation of a document using sklearn's TfidfVectorizer(we will be using our toy corpus).
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5. **[Pre-trained Word Embeddings](https://github.com/practical-nlp/practical-nlp/blob/master/Ch3/05_Pre_Trained_Word_Embeddings.ipynb)**: Here we demonstrate how we can represent text using pre-trained word embedding models and how to use them to get respresentations for the full text.
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5. **[Pre-trained Word Embeddings](https://github.com/practical-nlp/practical-nlp/blob/master/Ch3/05_Pre_Trained_Word_Embeddings.ipynb)**: Here we demonstrate how we can represent text using pre-trained word embedding models and how to use them to get representations for the full text.
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6. **[Custom Word Embeddings](https://github.com/practical-nlp/practical-nlp/blob/master/Ch3/06_Training_embeddings_using_gensim.ipynb)**: Here we demonstrate how to train a custom Word Embedding model(word2vec) using gensim on both, our toy corpus and a subset of wikipedia data.
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6. **[Custom Word Embeddings](https://github.com/practical-nlp/practical-nlp/blob/master/Ch3/06_Training_embeddings_using_gensim.ipynb)**: Here we demonstrate how to train a custom Word Embedding model(word2vec) using gensim on both, our toy corpus and a subset of Wikipedia data.
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7. **[Vector Representations via averaging](https://github.com/practical-nlp/practical-nlp/blob/master/Ch3/07_DocVectors_using_averaging_Via_spacy.ipynb)**: Here we demonstrate averaging of Document Vectors using spaCy.
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9. **[Visualizing Embeddings Using TSNE](https://github.com/practical-nlp/practical-nlp/blob/master/Ch3/09_Visualizing_Embeddings_Using_TSNE.ipynb)**: Here we demonstrate how we can use dimensionality reduction techniques such as TSNE to visualize embeddings.
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10. **[Visualizing Embeddings using Tensorboard](https://github.com/practical-nlp/practical-nlp/blob/master/Ch3/10_Visualizing_Embeddings_using_Tensorboard.ipynb)**: Here we demonstrate how we can visualize embeddings using Tensorboard.
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## 🖼️ Figures
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Color figures as requested by the readers.
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![figure](https://github.com/practical-nlp/practical-nlp-figures/raw/master/figures/3-1.png)
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![figure](https://github.com/practical-nlp/practical-nlp-figures/raw/master/figures/3-2.png)
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![figure](https://github.com/practical-nlp/practical-nlp-figures/raw/master/figures/3-3.png)
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![figure](https://github.com/practical-nlp/practical-nlp-figures/raw/master/figures/3-4.png)
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![figure](https://github.com/practical-nlp/practical-nlp-figures/raw/master/figures/3-5.png)
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![figure](https://github.com/practical-nlp/practical-nlp-figures/raw/master/figures/3-6.png)
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![figure](https://github.com/practical-nlp/practical-nlp-figures/raw/master/figures/3-7.png)
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![figure](https://github.com/practical-nlp/practical-nlp-figures/raw/master/figures/3-8.png)
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![figure](https://github.com/practical-nlp/practical-nlp-figures/raw/master/figures/3-9.png)
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![figure](https://github.com/practical-nlp/practical-nlp-figures/raw/master/figures/3-10.png)
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![figure](https://github.com/practical-nlp/practical-nlp-figures/raw/master/figures/3-11.png)
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![figure](https://github.com/practical-nlp/practical-nlp-figures/raw/master/figures/3-12.png)
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![figure](https://github.com/practical-nlp/practical-nlp-figures/raw/master/figures/3-13.png)
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![figure](https://github.com/practical-nlp/practical-nlp-figures/raw/master/figures/3-14.png)
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![figure](https://github.com/practical-nlp/practical-nlp-figures/raw/master/figures/3-15.png)
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![figure](https://github.com/practical-nlp/practical-nlp-figures/raw/master/figures/3-16.png)
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![figure](https://github.com/practical-nlp/practical-nlp-figures/raw/master/figures/3-17.png)
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![figure](https://github.com/practical-nlp/practical-nlp-figures/raw/master/figures/3-18.png)
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![figure](https://github.com/practical-nlp/practical-nlp-figures/raw/master/figures/3-19.png)
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