How to Improve the Performance of Deep Learning?

How to Improve the Performance of Deep Learning?

When it comes to the very common question of “how to improve the performance of deep learning?”, there are actually lots of ideas with which you can improve the performance of the algorithm. Let’s have a look at some of the most effective ideas for improving the performance of deep learning for a better software output.

Get More Data The quality of your algorithm model will depend on the type of data that you can get. Data training is very much important for the ultimate result. Deep learning and various other machine learning techniques of today get better with more amount of data. In short, the more data you have, the better the results. So, it can be said that with more amount of data, you can easily improve the performance of deep learning.

Data Rescaling Try to rescale all the data you have to the bounds of the activation functions. In case you are using function related to sigmoid activation, you can try by rescaling your data to the values between 0 and 1. If you are employing hyperbolic tangent, keep the value between -1 and 1.

For the best results, you can play around with the different sets of values such as: •Normal one scaled between 0 and 1 •Rescaled one with value between -1 and 1 •Standardized one according to your requirement

You can now easily evaluate the performance of the model with each of these value sets.

Keeping up bigger values in the network is not a good sign. Try to keep them as small as possible for smooth performance.

Selection of Feature The neural nets are very much robust to unrelated types of data. They will be using zero weight and also sideline the overall contribution of the attributes which are non-predictive.

There are various types of method for selection of feature that can give you the overall idea about which feature to keep in the algorithm and which not. Try to select those features that go well with the performance of your algorithm.

Problem Reframing Try to step back from the problems. It is not always right that the observations you have collected are the only way for framing your problems. It might also happen there are other framings that can better expose the structure of the problem and can help you learn about it.

So, do not just stick to those observations that you have collected. Try looking out for other ways too. Try to think within the problem along with the possible framings just before you choose the tool for the solution.

Spot Check the Algorithms There is no one in this world who knows which algorithm will perform the best for their problems. If there would have been someone who can actually trace out the perfect algorithm, then there would have been no concept of machine learning. There is not a single algorithm that can perform better than the other. Each and every algorithm that you use are the same.

So, you can choose the perfect algorithm by spot-checking them. Try to evaluate some of the linear methods just like the linear discriminate analysis. You can also evaluate various instance methods such as kNN and SVM. After you have found out the top performers, try to improve them for further preparation of data. Try to compare the results and you can easily develop a deep learning model.

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