Tuning Process (C2W3L01) | Summary and Q&A

August 25, 2017
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Tuning Process (C2W3L01)


This video provides guidelines for systematically organizing the hyperparameter tuning process, emphasizing the importance of random sampling over grid search and the possibility of using a coarse-to-fine search scheme.

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Key Insights

  • ☠️ Tuning hyperparameters in deep learning involves dealing with various parameters, including learning rate, momentum term, and number of layers.
  • ☠️ Prioritizing the tuning of important hyperparameters, such as the learning rate, can greatly impact performance.
  • 👨‍🔬 Random sampling is recommended for exploring hyperparameter values to ensure a more comprehensive search.
  • 👨‍🔬 A coarse-to-fine search scheme can help focus resources on promising regions of hyperparameter values.
  • 😫 The ultimate goal is to choose hyperparameter values that optimize the desired metric, whether it is based on training set objectives or development sets.
  • 💄 The importance of hyperparameters may vary across different applications, making it necessary to experiment and explore various values.
  • ❓ Different deep learning practitioners may have different intuitions and preferences for hyperparameter tuning.


hi and welcome back you've seen by now that changin your net can involve setting a lot of different hyper parameters now how do you go about finding a good setting for these hyper parameters in this video I want to share with you some guidelines some tips how to systematically organize your hyper parameter tuning process which hopefully will make i... Read More

Questions & Answers

Q: What are some of the important hyperparameters to tune in deep learning?

In deep learning, the learning rate, momentum term, and mini-batch size are important hyperparameters to tune. The learning rate has the most significant impact on performance.

Q: Why is random sampling preferred over grid search for hyperparameter tuning?

Random sampling allows for a more extensive exploration of hyperparameter values, especially when there are multiple hyperparameters. It increases the likelihood of finding values that work well for the most important hyperparameters.

Q: What is a coarse-to-fine search scheme?

A coarse-to-fine search scheme involves initially sampling hyperparameters across a wide range and then zooming in on a smaller region where the best-performing values are found. It allows for more focused exploration in areas of potential improvement.

Q: How can hyperparameter values be evaluated during tuning?

Hyperparameter values can be evaluated based on their performance on the training set objective or development sets. The goal is to pick values that optimize the desired metric.

Summary & Key Takeaways

  • Training deep nets involves setting various hyperparameters, such as learning rate, momentum term, and number of layers.

  • The most important hyperparameter to tune is the learning rate, followed by momentum term and mini-batch size.

  • Random sampling of hyperparameter values is preferred over grid search to explore a richer set of possibilities.

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