Viewing posts for the category Omarine User's Manual
The two most common transfer functions (activation functions) are the sigmoid logistic function or sigmoid function and ReLU function. Their derivative formulas are as follows
1) sigmoid function
We already know that although id3 is a legacy with limited capabilities, it is still the No. 1 candidate for distinguishing important features with a strong theoretical basis. So why not use it to remove irrelevant features?
Regularization is a measure of transforming the problem into a basic form that can be solved by known methods, which can be applied when certain assumptions are satisfied. For example linearization of data in linear regression.
In machine learning (deep learning) there is no specific way to do that. It is desirable to remove less relevant or irrelevant features to simplify the problem. Since then name the method. A typical example is Google's "L1 Regularization" method, which has been detailed in the article Neural network: Using genetic algorithms to train and deploy neural networks: Need the test set? so I don't repeat it here. One thing can be seen immediately that the "L1 Regularization" method eliminates the less relevant features only after they have been put into the network. What do you think about this? Put noise into the network and then find a way to remove it! How many features are less relevant? Which ones?
If there are indeed less relevant features then id3 is a great way to remove them. This is done in the data mining step, ie before putting data into the network for training. You can identify these less relevant features by programming or using the tool fpp. Removed features will not be present in the id3's output
Many data scientists confuse in distinguishing between Classification neural network and Regression neural network. There are several reasons:
Currently the popular output class encoding method is one-hot, each class corresponds to a vector that only one bit of the class is turned on (by 1), while the other bits are 0. The most applicable in this way is TensorFlow with one_hot () function
The output is a level 4 square matrix, each row representing one class. Suppose those classes have the following labels:
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