Answers.Body: <p>To the best of my knowledge:</p>
  <p>A latent representation, or code, <span class="math-container">$\mathbb{z}$</span> is a learned continuous vector representation of some input <span class="math-container">$\mathbb{x}$</span>. E.g., we might have a code of an image.</p>
  <p>An embedding is a type of code which specifically represents a discrete variable <span class="math-container">$\mathbb{x}_\text{discrete}$</span>. One popular example is a word embedding, where words are encoded discretely using one-hot encoding.</p>
  
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Answers.UserDisplayName: Kaden McKeen
Answers.Body: <p>Vector representation is a generic term used to talk about any type of feature encoding, embedding vectors are instead a special case of vector representation.</p>
  <p>When talking about vector representation the only underlying assumption is that every variable was encoded into numerical values, without any restriction regarding the numbers or the vector itself.</p>
  <p>Embedding vectors instead are specifically continuous vectors of fixed dimensions obtained trough matrix factorization techniques or deep learning models. They originally proposed to encode text in the <a href="https://arxiv.org/pdf/1301.3781.pdf" rel="nofollow noreferrer">Word2Vec</a> paper, and since then they acquired more and more popularity due to the high generalization potential of the proposed method in other AI branched rather than natural language processing.</p>
  
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Answers.UserDisplayName: Edoardo Guerriero
Body: <p>As I searched about this two terms, I found they are somehow like each other, both try to create a vector from raw data as I understood. But, what is the difference of this two term?</p>
  
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Comments.CreationDate: 2022-07-29T10:50:46.630Z
Comments.Text: Have you read https://datascience.stackexchange.com/a/54045/58034 and page 2 of: https://arxiv.org/abs/1905.06147 and https://cloud.google.com/architecture/overview-extracting-and-serving-feature-embeddings-for-machine-learning - You are allowed to answer your own question.
Comments.UserDisplayName: Rob
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Comments.Text: @Rob
  thank you Rob, I read both of them now, and still it is complicated, I still think their definitions are like each other, can you give me a hint? any short answer can be really helpful.
Comments.UserDisplayName: aliiiiiiiiiiiiiiiiiiiii
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Comments.Text: Explained in the first five parts: https://developers.google.com/machine-learning/crash-course/embeddings/video-lecture or see:  https://www.toptal.com/machine-learning/embeddings-in-machine-learning  https://analyticsindiamag.com/machine-learning-embedding/ - embeddings *group* the representation (the data set) into categories seperated by varying distances, often with partial overlap between individual embeds.
Comments.UserDisplayName: Rob
Tags: <machine-learning><deep-learning><terminology><embeddings><representation-learning>
Title: What is the difference between representation and embedding?
UserDisplayName: aliiiiiiiiiiiiiiiiiiiii
ThreadText: # What is the difference between representation and embedding?
  
  (Asked by: username_0 on 2022-07-29)
  
  As I searched about this two terms, I found they are somehow like each other, both try to create a vector from raw data as I understood. But, what is the difference of this two term?
  
  **Tags:** machine-learning, deep-learning, terminology, embeddings, representation-learning
  > **Comments:**
  > **username_1**: Have you read https://datascience.stackexchange.com/a/54045/58034 and page 2 of: https://arxiv.org/abs/1905.06147 and https://cloud.google.com/architecture/overview-extracting-and-serving-feature-embeddings-for-machine-learning - You are allowed to answer your own question.  
  > **username_0**: @Rob thank you Rob, I read both of them now, and still it is complicated, I still think their definitions are like each other, can you give me a hint? any short answer can be really helpful.  
  > **username_1**: Explained in the first five parts: https://developers.google.com/machine-learning/crash-course/embeddings/video-lecture or see:  https://www.toptal.com/machine-learning/embeddings-in-machine-learning  https://analyticsindiamag.com/machine-learning-embedding/ - embeddings *group* the representation (the data set) into categories seperated by varying distances, often with partial overlap between individual embeds.  
  
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  ## 2 answers
  
  ### Answer by username_3 on 2022-07-29. Score: 3 | Accepted answer
  
  Vector representation is a generic term used to talk about any type of feature encoding, embedding vectors are instead a special case of vector representation.
  
  When talking about vector representation the only underlying assumption is that every variable was encoded into numerical values, without any restriction regarding the numbers or the vector itself.
  
  Embedding vectors instead are specifically continuous vectors of fixed dimensions obtained trough matrix factorization techniques or deep learning models. They originally proposed to encode text in the [Word2Vec](https://arxiv.org/pdf/1301.3781.pdf) paper, and since then they acquired more and more popularity due to the high generalization potential of the proposed method in other AI branched rather than natural language processing.
  
  
  ---
  
  ### Answer by username_2 on 2022-12-15. Score: 0
  
  To the best of my knowledge:
  
  A latent representation, or code, $\mathbb{z}$ is a learned continuous vector representation of some input $\mathbb{x}$. E.g., we might have a code of an image.
  
  An embedding is a type of code which specifically represents a discrete variable $\mathbb{x}\_\text{discrete}$. One popular example is a word embedding, where words are encoded discretely using one-hot encoding.
  
  
  ---
  
  