To be added
Set of notebooks associated with the chapter.
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One-Hot Encoding: 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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Bag of Words : Here we demonstrate how to arrive at the bag of words representation for our toy corpus.
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Bag of N Grams: Here we demonstrate how Bag of N-Grams work using our toy corpus.
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TF-IDF: 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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Pre-trained Word Embeddings: 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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Custom Word Embeddings: 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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Vector Representations via averaging: Here we demonstrate averaging of Document Vectors using spaCy.
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Doc2Vec Model: Here we demonstrate how to train your own doc2vec model.
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Visualizing Embeddings Using TSNE: Here we demonstrate how we can use dimensionality reduction techniques such as TSNE to visualize embeddings.
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Visualizing Embeddings using Tensorboard: Here we demonstrate how we can visualize embeddings using Tensorboard.
Color figures as requested by the readers.
