The Illustrated Word2vec – Jay Alammar – Visualizing machine learning one concept at a time

“There is in all things a pattern that is part of our universe. It has symmetry, elegance, and grace - those qualities you find always in that which the true artist captures. You can find it in the turning of the seasons, in the way sand trails along a ridge, in the branch clusters of the creosote bush or the pattern of its leaves. We try to copy these patterns in our lives and our society, seeking the rhythms, the dances, the forms that comfort. Yet, it is possible to see peril in the finding of ultimate perfection. It is clear that the ultimate pattern contains it own fixity. In such perfection, all things move toward death.” ~ Dune (1965) I find the concept of embeddings to be one of the most fascinating ideas in machine learning. If you’ve ever used Siri, Google Assistant, Alexa, Google Translate, or even smartphone keyboard with next-word prediction, then chances are you’ve benefitted from this idea that has become central to Natural Language Processing models.

55 mentions: @jalammar@cnvrg_io@ahamsiva@norespal@praetorian@joshuamschultz@marinbenc@mgoadric
Date: 2019/03/27 08:17

Referring Tweets

@jalammar The Illustrated Word2vec t.co/OyWncSFOPn New blog post gently introducing word embeddings, and visualizing the mechanics of the pivotal NLP algorithm that is finding new life in recommender systems. #nlproc #thkaa t.co/kLu4ESCkwf
@cnvrg_io 🙋Raise your hand if you find the concept of embeddings to be one of the most fascinating ideas in machine learning 🙋 t.co/cemiC5d0Uj #MachineLearning #Embeddings #Patterns #DataScience
@norespal From words to numbers to insights: the magic of deep learning for natural language processing t.co/UXclfzHY95
@praetorian This visual introduction to Word2Vec and Word embeddings is very helpful. I wish I had it when I was starting to work with them t.co/2j8nKjlagn
@marinbenc This lovely explanation of word2vec and embedding in ML made me want to re-read Dune! t.co/TraVaUIyPh
@mgoadric Excellent visual explanation of Word2Vec, a mathematical framework for capturing semantic word similarity: t.co/mjbDP9wLmJ

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