Blog: Nlp
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Word2Vec: Why Do We Need Word Representations?
Word2vec, short for "word to vector," is a technology used to represent the relationships between different words in the form of a graph. This technology is widely used in machine learning for embedding and text analysis.
- haskell
- machine learning
- serokell
- rust
- elixir
- haskell in production
- blockchain
- introduction
- algorithms
- edsl
- erlang
- ghc
- neural networks
- web development
- computer science
- data science
- elixir tutorial
- functional futures
- mathematics
- resource guide
- tezos
- elixir in production
- lorentz
- parsers
- smart contracts
- dependent types
- elixir software
- functional programming
- haskell software
- history
- library
- metaprogramming
- nix
- remote work
- rust in production
- template haskell
- typescript
- agda
- computer vision
- deep learning
- formal verification
- ml resources
- what's that typeclass
- big data
- conferences
- idris
- image generation
- learn haskell
- logic
- ml applications
- open source projects
- phoenix
- top projects
- trends
- type families
- ai ethics
- biotech
- dependent haskell
- ecto
- education
- events
- generative ai
- ml algorithms
- morley
- no code
- ocaml
- optimization
- outsourcing
- pattern recognition
- physics
- rust software
- rust tutorial
- scala
- testing
- ton
- topology
- transformers
- webassembly
- women in tech
- ai
- cardano
- cnn
- compilers
- coq
- cryptography
- data analytics
- data mining
- design
- dlt
- existential types
- feature engineering
- fintech
- free monads
- game development
- genetics
- github
- gitlab
- gleam
- gpt
- higher-rank types
- hobby
- icfpc
- lambda calculus
- lean
- lisp
- michelson
- ml
- ml ideas
- ml projects
- mtl
- nlp
- purescript
- quantum computers
- random numbers
- reason
- serokellchat
- servant
- signal processing
- supervised learning
- support vector machine
- tagless final
- text analysis
- text-to-speech
- unsupervised learning
- web summit
- web3
+ More