What is Word Embedding?

Figure 1. Converting categorical data into numerical values
Figure 2. A word space representation
Figure 3. A more realistic word space representation
Figure 4. Words close to the word “artificial”
Figure 5. 2-gram outputs created by the corpus of “artificial intelligence” and “chemistry”
Figure 6. Vector showing the position of the word “artificial” in the word space.
Figure 7. Words that are close to the word “artificial” with the FastText model
Figure 8. Word space in the model
Figure 9. Words close to the word “artificial” in “artificial intelligence” and “chemistry” models
Figure 10. Words that are close to the misspelled word “artifcial”
KeyError: “word ‘artifcial’ not in vocabulary”





Love podcasts or audiobooks? Learn on the go with our new app.

Recommended from Medium

Dig deeper in Federated Learning, Advantages and Challenges

Domain Adaptation

Data Labeling — Essential for improving your ML algorithms

Form of… An Innacurate Prediction!

Explainable AI: Saliency Maps

Seeing is Believing: Industrial Applications of Machine Vision and Learning

Micro-Tutorial: Quick Text Preprocessing with NLTK

The crux of word embedding layers -Part 1

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Ahmet Tuğrul Bayrak

Ahmet Tuğrul Bayrak


More from Medium

ML Models on Petabytes of Data — You Need GPUs

Wrapping Up 2021 Product Highlights | Dataloop Blog

Machine Learning Explainability for smart business

MT Metrics Explained: A Visit to the MT Metric Zoo