Answers.Body: <p>I'm not aware of a direct way for finding the best NN architecture for a given task, but the recommended way, as far as I know, is to devise a network that can overfit the training data, and then apply regularization on top of it.</p>
  <p>That way, you can be almost sure you're not underfitting/underperforming due to network capacity.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-03-31T02:51:52.447Z
Answers.LastActivityDate: 2021-03-31T13:41:59.803Z
Answers.LastEditDate: 2021-03-31T13:41:59.803Z
Answers.UserDisplayName: SpiderRico
Answers.Body: <p>It's harder to overfit it certainly!</p>
  
  <p>I mean practically speaking there has to be some assumptions on the generation model of your data, either explicit or implicit.</p>
  
  <p>I would try probably 1-2 layer network first(maybe your data is linearly separable if you're lucky). </p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-03-05T21:34:18.527Z
Answers.LastActivityDate: 2020-03-05T21:34:18.527Z
Answers.UserDisplayName: FourierFlux
Body: <p>I'm currently trying to predict 1 output value with 52 input values. The problem is that I only have around 100 rows of data that I can use. </p>
  
  <p>Will I get more accurate results when I use a small architecture than when I use multiple layers with a higher amount of neurons? </p>
  
  <p>Right now, I use 1 hidden layer with 1 neuron, because of the fact that I need to solve (in my opinion) a basic regression problem. </p>
  
Tags: <neural-networks><ai-design><datasets><regression><architecture>
Title: Is a basic neural network architecture better with small datasets?
UserDisplayName: Yari Nowicki
ThreadText: # Is a basic neural network architecture better with small datasets?
  
  (Asked by: username_0 on 2020-03-05)
  
  I'm currently trying to predict 1 output value with 52 input values. The problem is that I only have around 100 rows of data that I can use.
  
  Will I get more accurate results when I use a small architecture than when I use multiple layers with a higher amount of neurons?
  
  Right now, I use 1 hidden layer with 1 neuron, because of the fact that I need to solve (in my opinion) a basic regression problem.
  
  **Tags:** neural-networks, ai-design, datasets, regression, architecture
  
  ---
  
  ## 2 answers
  
  ### Answer by username_1 on 2021-03-31. Score: 1
  
  I'm not aware of a direct way for finding the best NN architecture for a given task, but the recommended way, as far as I know, is to devise a network that can overfit the training data, and then apply regularization on top of it.
  
  That way, you can be almost sure you're not underfitting/underperforming due to network capacity.
  
  
  ---
  
  ### Answer by username_2 on 2020-03-05. Score: 0
  
  It's harder to overfit it certainly!
  
  I mean practically speaking there has to be some assumptions on the generation model of your data, either explicit or implicit.
  
  I would try probably 1-2 layer network first(maybe your data is linearly separable if you're lucky).
  
  
  ---
  
  