Logistic classification
GitHub Project Address: https://github.com/ahangchen/GDLnotes
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About
Simple but important classifier
- Train your first simple model entirely end to end
- Download, preprocess some pictures to classify
- Run an actual logistic classifier on images data
- Connect bit of math and code
Detaillinear Classifier
This is modeled because linear formulas are the simplest mathematical models, and that's all.
Softmax
- Code SOFT_MAX.PY:SOFTMAX Implementation and application
- The greater the difference in the score of input (which can be multiplied by 10), the greater the difference in the probability of each label of the output, the less the difference
- Softmax only cares about the probability of a few labels and doesn't care about the specific values
- Machine learning is a matter of increasing the success rate of prediction, so it is a process that makes the difference between score
One hot encoding
The correct prediction result should be that only one label is set, and the other label is not set. In this case, the most probable result is the most predictable probability.
Example:take this test
Summary
Find the appropriate W and B, which makes the average of the distance D of S and L, the smallest in the entire data set N.
Minimized cross-entropy
The average of D is training loss, sum and matrix multiplication is a big data activity.
The error of two parameters results in a rounded loss, so all we have to do is find the weight that is as close to the center as possible.
Machine learning problem becomes a numerical optimization
-One solution: Gradient descent, derivative
Modify the parameters to check if the error is larger and change to a smaller direction until you reach bottom.
The weight is two-dimensional, but in fact there may be a great many weight
Next section Practice
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[Google Deep Learning notes] Logistic classification