Difference between revisions of "Bag-of-Words (BoW)"
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| − | [ | + | {{#seo: |
| + | |title=PRIMO.ai | ||
| + | |titlemode=append | ||
| + | |keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS | ||
| + | |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | ||
| + | }} | ||
| + | [https://www.youtube.com/results?search_query=Bag+Words+bow+nlp+natural+language YouTube search...] | ||
| + | [https://www.google.com/search?q=Bag+Words+bow+nlp+natural+language ...Google search] | ||
| − | * [[Natural Language Processing (NLP) | + | * [[Large Language Model (LLM)]] ... [[Natural Language Processing (NLP)]] ...[[Natural Language Generation (NLG)|Generation]] ... [[Natural Language Classification (NLC)|Classification]] ... [[Natural Language Processing (NLP)#Natural Language Understanding (NLU)|Understanding]] ... [[Language Translation|Translation]] ... [[Natural Language Tools & Services|Tools & Services]] |
| − | * [ | + | * [[Python#scikit-learn|scikit-learn]] |
| − | + | * [[Term Frequency, Inverse Document Frequency (TF-IDF)]] | |
* [[Word2Vec]] | * [[Word2Vec]] | ||
* [[Doc2Vec]] | * [[Doc2Vec]] | ||
* [[Skip-Gram]] | * [[Skip-Gram]] | ||
* [[Global Vectors for Word Representation (GloVe)]] | * [[Global Vectors for Word Representation (GloVe)]] | ||
| + | * [[Feature Exploration/Learning]] | ||
| + | * [https://pathmind.com/wiki/bagofwords-tf-idf A Beginner's Guide to Bag of Words & TF-IDF | Chris Nicholson - A.I. Wiki pathmind] | ||
| + | [[Python#scikit-learn|scikit-learn]]: Bag-of-Words = Count Vectorizer | ||
| + | |||
| + | One common approach for exBag-of-Wordstracting features from text is to use the bag of words model: a model where for each document, an article in our case, the presence (and often the frequency) of words is taken into consideration, but the order in which they occur is ignored. | ||
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| + | <youtube>aCdg-d_476Y</youtube> | ||
<youtube>OGK9SHt8SWg</youtube> | <youtube>OGK9SHt8SWg</youtube> | ||
| − | <youtube> | + | <youtube>9Z1MgTGQHQI</youtube> |
| + | <youtube>IZAKJMgUmWc</youtube> | ||
Latest revision as of 14:29, 28 April 2023
YouTube search... ...Google search
- Large Language Model (LLM) ... Natural Language Processing (NLP) ...Generation ... Classification ... Understanding ... Translation ... Tools & Services
- scikit-learn
- Term Frequency, Inverse Document Frequency (TF-IDF)
- Word2Vec
- Doc2Vec
- Skip-Gram
- Global Vectors for Word Representation (GloVe)
- Feature Exploration/Learning
- A Beginner's Guide to Bag of Words & TF-IDF | Chris Nicholson - A.I. Wiki pathmind
scikit-learn: Bag-of-Words = Count Vectorizer
One common approach for exBag-of-Wordstracting features from text is to use the bag of words model: a model where for each document, an article in our case, the presence (and often the frequency) of words is taken into consideration, but the order in which they occur is ignored.
