# cosine text similarity

Since cosine text similarity is a very common topic, there already should be very good videos available.  We  give here some recommendations for didactical videos explaining cosine text similarity. 



* [Minsuk Heo 허민석: How to measure similarity in vector space (cosine similarity)](https://www.youtube.com/watch?v=xY3jrJdpuQg): Introduce Euclidean Distance and Cosine similarity with easy example for easy understanding to NLP (natural language processing) deep learning students. (4:16)
* [Cosine similarity, cosine distance explained](https://www.youtube.com/watch?v=m_CooIRM3UI): (14:27) Cosine similarity, cosine distance explained in a way that high school student can also understand it easily. If you have aspirations of becoming a data scientist, you must know these two concepts. I will explain cosine similarity using a real life simple example and then we will write code in python using sklearn library.
* [Francisco Iacobelli: Cosine Similarity](https://www.youtube.com/watch?v=5lvS8078ykA): Introduction to document similarity using document x term matrices. I'll introduce intuitions and end with cosine similarity as a better measure for similarity. (23:31)

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Erklärungen incl. Transkript:

* [Feature Extraction from Text (USING PYTHON)](https://www.youtube.com/watch?v=7YacOe4XwhY) (14:29)
* [NLP: Understanding the N-gram language models](https://www.youtube.com/watch?v=GiyMGBuu45w) (10:32): incl. transkript  


Vom Wort zum Topic:

* [ Minsuk Heo 허민석: Latent Semantic Analysis](https://www.youtube.com/watch?v=OvzJiur55vo): Understand LSA (a.k.a LSI) for topic modeling and topic similarity.