Body: <p>I aim to use variational autoencoders (VAE) to find interpretable latent spaces for genetic data. So, I need to understand how they work, what activation function to use, etc. But all tutorials and courses that explain VAE use image data as their examples. Are there any good resources (courses, youtube videos, tutorials) that use non-image data? A proficiency level does not matter.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-08-14T19:11:17.220Z
Comments.Text: how is a table different from an image?
Comments.UserDisplayName: Alberto
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-08-14T20:45:44.340Z
Comments.Text: in genetic data I have features and observations, so I can get latent spaces with weighted genes in them. I understand that an image also is a matrix of pixels, but I think models using images often have a purpose to reproduce/denoise/classify. I am interested in explainable latent spaces
Comments.UserDisplayName: Yulia Kentieva
Tags: <tensorflow><pytorch><autoencoders><variational-autoencoder><training-datasets>
Title: Any tutorials/courses to learn variational autoencoders on tabular data?
UserDisplayName: Yulia Kentieva
ThreadText: # Any tutorials/courses to learn variational autoencoders on tabular data?
  
  (Asked by: username_0 on 2024-08-14)
  
  I aim to use variational autoencoders (VAE) to find interpretable latent spaces for genetic data. So, I need to understand how they work, what activation function to use, etc. But all tutorials and courses that explain VAE use image data as their examples. Are there any good resources (courses, youtube videos, tutorials) that use non-image data? A proficiency level does not matter.
  
  **Tags:** tensorflow, pytorch, autoencoders, variational-autoencoder, training-datasets
  > **Comments:**
  > **username_1**: how is a table different from an image?  
  > **username_0**: in genetic data I have features and observations, so I can get latent spaces with weighted genes in them. I understand that an image also is a matrix of pixels, but I think models using images often have a purpose to reproduce/denoise/classify. I am interested in explainable latent spaces  
  
  ---
  
  ## 0 answers
  
  