Authors

• Anmol Gulati
• Ben Kochavy
• Bhargav Srinivasa
• Chaitali Saini
• Chinmaya Pancholi
• Chris Lakatos
• Denis Kuramshin
• Devashish Deshpande
• Dmitry Berdov
• Filip Štefaňák
• fruitful
• Jan Pomikálek
• Jaroslav Dostál
• Jayant Jain
• Jeff Hoey
• Ji Xiaohong
• Jill Myllyoja
• Jimmy
• Lev Konstantinovskiy
• Mohit Rathore
• Ólavur Mortensen
• Parul Sethi
• Prakhar Pratyush
• Pranay, Aman and Aayush
• Radim Řehůřek
• Shiva Manne
• Šimon Pavlík
• Tony DiLoreto

Pages

• Blog
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• Case study: O2
• Case study: Thermo Fisher Scientific
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Posts

• Datasets

  • New download API for pretrained NLP models and datasets in Gensim

• Deep Learning

  • Personal Data Analytics
  • Machine learning mega-benchmark: GPU providers (part 2)
  • Semantic Search Using a Fulltext Engine Presented at ACL 2017

• gensim

  • Pivoted document length normalisation
  • Docstrings in open source Python
  • Gensim Survey 2018
  • Implementing Poincaré Embeddings
  • Translation Matrix: how to connect “embeddings” in different languages?
  • Chinmaya’s GSoC 2017 Summary: Integration with sklearn & Keras and implementing fastText
  • Parul’s GSoC 2017 summary: Training and Topic visualizations in gensim
  • Chinmaya’s Google Summer of Code 2017 Live-Blog : a Chronicle of Integrating Gensim with scikit-learn and Keras
  • Parul’s Google Summer of Code 2017 Live-Blog : a chronicle of adding training and topic visualizations in gensim
  • Google Summer of Code 2017 – Performance improvement in Gensim and fastText
  • Google Summer of Code 2017 – Week 1 of Integrating Gensim with scikit-learn and Keras
  • Text Summarization in Python: Extractive vs. Abstractive techniques revisited
  • Gensim switches to semantic versioning
  • WordRank embedding: “crowned” is most similar to “king”, not word2vec’s “Canute”
  • New Gensim feature: Author-topic modeling. LDA with metadata.
  • Topic Modelling with Latent Dirichlet Allocation: How to pre-process data and tune your model. New tutorial.
  • Author-topic models: why I am working on a new implementation
  • Gensim at PyCon France 2016
  • Three Sprints in India (To Say Nothing of PyCon)
  • Gensim word2vec on CPU faster than Word2veckeras on GPU (Incubator Student Blog)
  • FastText and Gensim word embeddings
  • More topic coherence use-cases
  • Validating gensim’s topic coherence pipeline
  • The craziness that is Dynamic Topic Models
  • What is Topic Coherence?
  • Radim, Gensim and RaRe Technologies
  • Devashish’s Student Incubator Live-Blog: a Chronicle of Implementing Topic Coherence Metrics in Gensim
  • Pycon 2016 and Gensim Sprint Recap
  • Topic Coherence API Project – Week 2
  • Topic Modelling and Coloring Document Words
  • 2016 Student Data Science Programs with RaRe Technologies
  • 2016 Student Incubator – Week 1 Implementing Topic Coherence Metrics in Gensim
  • Does Python Stand a Chance in Today’s World of Data Science? [video]
  • Making sense of word2vec
  • Doc2vec tutorial
  • Multicore LDA in Python: from over-night to over-lunch
  • Data streaming in Python: generators, iterators, iterables
  • Tutorial on Mallet in Python
  • Word2vec Tutorial
  • Performance Shootout of Nearest Neighbours: Querying
  • Asymmetric LDA Priors, Christmas Edition
  • Performance Shootout of Nearest Neighbours: Contestants
  • Performance Shootout of Nearest Neighbours: Intro
  • Five Years of Gensim
  • Parallelizing word2vec in Python
  • Word2vec in Python, Part Two: Optimizing
  • Deep learning with word2vec and gensim

• Go

  • Go, Games, Strategy and Life: The Big Picture

• Google Summer of Code

• Google Summer of Code 2016

  • Dynamic NMF and Dynamic Topics
  • Bhargav’s Google Summer of Code 2016 Live-Blog: a Chronicle of Dynamic Topic Models
  • Understanding and Coding Dynamic Topic Models
  • Google Summer of Code 2016 – Week 1 on Dynamic Topic Models

• Machine Learning

  • Counting Efficiently with Bounter pt. 2: CountMinSketch
  • Machine learning benchmarks: Hardware providers (part 1)
  • The Mummy Effect: Bridging the gap between academia and industry (PyData keynote)
  • Sharing Facets Dive Visualizations
  • Counting Efficiently with Bounter pt. 1: HashTable
  • Data analysis in Python: Interactive confusion matrix with Facets Dive, Pandas, Scikit-learn
  • RaRe Technologies Announces New Growth Team and Pycon Participation for 2016

• Open Source

  • Dealing mergeytocin: how to run an open source sprint. Based on 8 gensim sprints in 5 countries in 12 months.

• personal data

  • Scanning Office 365 for sensitive PII information

• Personal Data Protection

  • Export PII drill-down reports

• PII Tools

• podcast

  • RRP #4: Leonid Boytsov on kNN search and information retrieval
  • RRP #3: Andy Müller on scikit-learn and open source
  • Archive of RRP Podcast Episodes
  • RRP #2: John D. Cook on math consulting, Python and going solo
  • RRP #1: Tomáš Mikolov on word2vec and AI research at Microsoft, Google, Facebook

• politics

  • Technology vs. politics, round 1

• programming

  • Text Summarization with Gensim

• PyCon

• ScaleText

• security

• startups

  • Money, startups, fame, bullshit

• Student Incubator

• summarization

• Uncategorized

  • Site under construction