Answers.Body: <p>Such a system can and does solve the problem of hyperparameter tuning. <a href="https://cloud.google.com/automl/" rel="nofollow noreferrer">Google's AutoML</a> does this. <a href="https://blog.coast.ai/lets-evolve-a-neural-network-with-a-genetic-algorithm-code-included-8809bece164" rel="nofollow noreferrer">Here is another example that uses a Genetic Algorithm</a> to breed new neural network structures. 
  AutoML has been shown to outperform humans in the rate that it improves network designs. It seems to favour Residual Network style topologies. </p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-06-20T07:10:56.820Z
Answers.LastActivityDate: 2019-06-20T07:10:56.820Z
Answers.UserDisplayName: DrMcCleod
Body: <p>It seems to me that, right now, the key to making a good Machine Learning model is in choosing the right combination of hyper-parameters.</p>
  
  <p>Firstly: Am I right in saying, if a model is able to tune it's own hyper-parameters, we have in some sense achieved a general intelligence system? Or a glimpse of an actual artificial intelligence system? </p>
  
  <p>I feel the answer lies in what one means by " tune it's own hyper-parameters". If it means to be able to reach Bayesian levels of performance on that task then theoretically, after the tuning, the model is able to perform at par or better than humans and so it seems the answer would be yes.</p>
  
  <p>Secondly: I understand that hyper-parameter tuning is done intuitively. But there are a  set of general directions that is discernable looking at results.  Here I am talking about a heuristic approach to perfect a learning model. 
  Consider an example:
  Say I hardcode a model to, while training, observe gradient values. If the gradient is too large or the cost is highly oscillatory, then restart training with a smaller learning rate.
  Then obtain metrics on a test set. If it is poor, then again restart training with regularisation or increased regularisation. 
  It can also observe various plot behaviours, etc.</p>
  
  <p>The point is maybe not every trick up a researcher's sleeve can be hardcoded. But a decent level of basic tuning can be done. </p>
  
  <p>Thirdly: Let us say, we have a reinforcement learning system on top of a supervised learning system. That is an RL network sets some hyper-parameters. The action then is to train with these hyper-parameters. The reward would be the accuracy on the test set. </p>
  
  <p>Is it possible that such a system could solve the problem of hyper-parameter tuning? </p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-06-20T07:03:46.603Z
Comments.Text: I don't know if that's what you're asking for, but neuroevolution may be something of your interest
Comments.UserDisplayName: olinarr
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-06-19T22:58:55.613Z
Comments.Text: It's not clear exactly what you're asking. I know that this is an old post, but, please, edit your post to ask a clear question. Also, remove the first question, which seems to be completely unrelated.
Comments.UserDisplayName: nbro
Tags: <machine-learning><reinforcement-learning><hyperparameter-optimization>
Title: Evolving Machine Learning
UserDisplayName: pranav
ThreadText: # Evolving Machine Learning
  
  (Asked by: username_0 on 2019-06-20)
  
  It seems to me that, right now, the key to making a good Machine Learning model is in choosing the right combination of hyper-parameters.
  
  Firstly: Am I right in saying, if a model is able to tune it's own hyper-parameters, we have in some sense achieved a general intelligence system? Or a glimpse of an actual artificial intelligence system?
  
  I feel the answer lies in what one means by " tune it's own hyper-parameters". If it means to be able to reach Bayesian levels of performance on that task then theoretically, after the tuning, the model is able to perform at par or better than humans and so it seems the answer would be yes.
  
  Secondly: I understand that hyper-parameter tuning is done intuitively. But there are a set of general directions that is discernable looking at results. Here I am talking about a heuristic approach to perfect a learning model.
  Consider an example:
  Say I hardcode a model to, while training, observe gradient values. If the gradient is too large or the cost is highly oscillatory, then restart training with a smaller learning rate.
  Then obtain metrics on a test set. If it is poor, then again restart training with regularisation or increased regularisation.
  It can also observe various plot behaviours, etc.
  
  The point is maybe not every trick up a researcher's sleeve can be hardcoded. But a decent level of basic tuning can be done.
  
  Thirdly: Let us say, we have a reinforcement learning system on top of a supervised learning system. That is an RL network sets some hyper-parameters. The action then is to train with these hyper-parameters. The reward would be the accuracy on the test set.
  
  Is it possible that such a system could solve the problem of hyper-parameter tuning?
  
  **Tags:** machine-learning, reinforcement-learning, hyperparameter-optimization
  > **Comments:**
  > **username_1**: I don't know if that's what you're asking for, but neuroevolution may be something of your interest  
  > **username_2**: It's not clear exactly what you're asking. I know that this is an old post, but, please, edit your post to ask a clear question. Also, remove the first question, which seems to be completely unrelated.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_3 on 2019-06-20. Score: 2
  
  Such a system can and does solve the problem of hyperparameter tuning. [Google's AutoML](https://cloud.google.com/automl/) does this. [Here is another example that uses a Genetic Algorithm](https://blog.coast.ai/lets-evolve-a-neural-network-with-a-genetic-algorithm-code-included-8809bece164) to breed new neural network structures.
  AutoML has been shown to outperform humans in the rate that it improves network designs. It seems to favour Residual Network style topologies.
  
  
  ---
  
  