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1. Topic Modeling with BERT. | Towards Data Science

   2020-12-08 Tags: topic modeling, bert, classification, nlp, embedding, deep learning, tf-idf, clustering, marten grootendorst by klotz
2. PolyFuzz: String matching, grouping, and evaluation. | Towards Data Science

   2020-12-08 Tags: string, matching, machine learning, polyfuzz, python, transformer, levenstein distance, text analysis, similarity, tf-idf, rapidfuzz, fasttext by klotz
3. Text Classification with NLP: Tf-Idf vs Word2Vec vs BERT | by Mauro Di Pietro | Jul, 2020 | Towards Data Science

   2020-07-19 Tags: text, classification, nlp, tf-idf, word2vec, bert by klotz
4. duplicate-code-detection-tool/duplicate_code_detection.py at master · platisd/duplicate-code-detection-tool · GitHub

   A simple Python3 tool to detect similarities between files within a repository.
   Document similarity code adapted from Jonathan Mugan's tutorial:
   https://www.oreilly.com/learning/how-do-i-compare-document-similarity-using-python
   '''

   2020-03-11 Tags: python, code, similarity, tf-idf, document by klotz
5. “A Game of Words: Vectorization, Tagging, and Sentiment Analysis”

   2019-07-22 Tags: word embedding, tf-idf, kaggle, text understanding, game of thrones, nlp by klotz
6. [NLP] Performance of Different Word Embeddings on Text Classification

   2019-07-11 Tags: word embedding, tutorial, tf-idf by klotz
7. How I used machine learning to classify emails and turn them into insights (part 1).

   2018-11-07 Tags: machine learning, tf-idf, k-means, text, nlp, classifier, email by klotz
8. Applying Machine Learning to classify an unsupervised text document

   2018-11-03 Tags: machine learning, nlp, tf-idf, classification, k-means, blog by klotz
9. Comparing the performance of non-supervised vs supervised learning methods for NLP text…

   2018-10-19 Tags: nlp, machine learning, tf-idf, classification, k-means, pca, lda by klotz
10. Document Clustering with Python

    tokenizing and stemming each synopsis
    transforming the corpus into vector space using tf-idf
    calculating cosine distance between each document as a measure of similarity
    clustering the documents using the k-means algorithm
    using multidimensional scaling to reduce dimensionality within the corpus
    plotting the clustering output using matplotlib and mpld3
    conducting a hierarchical clustering on the corpus using Ward clustering
    plotting a Ward dendrogram
    topic modeling using Latent Dirichlet Allocation (LDA)

    2018-08-16 Tags: lda, document, clustering, python, tf-idf, k-means, nlp, text by klotz

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