- A wonderful activation function that turns numbers aka logits into probabilities that sum to one.
- Outputs a vector that represents the probability distributions of a list of potential outcomes
- Core element that is used in deep learning task
Lets predict a model to see if we receive a gift or not?
MODEL PREDICTIONS
The probability that we get a gift is 0.8
The probability that we would not receive a gift is 0.2
JOB OF MODEL
Based on the existing features (Birthdate,year) it calculates the linear model which would be the score
Then the probability that we get a gift or not is the sigmoid function applied to that score
MODEL WHICH WOULD TELL US WHICH ANIMAL WHAT WE SAW
- Assuming we have a linear model based on some inputs, We calculate linear function based on inputs like if it has a feather, teeth etc
- After calculating linear functions based on these inputs , assuming that we get some scores
Note:
- We need to change the scores to 1. (Requirement for probability)
- The probability of duck is higher then beaver and the beaver is higher then walrus
We can take the sum of scores and divide it by zero
The probability would be 2/3 for the duck 1/3 for the beaver and 0 for the walrus
NOTE:
We need to store these scores into positive scores. This can be achieved using exponential function
It is called as softmax function
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The article gives a clear introduction to the Softmax function and explains how model scores can be converted into probabilities for multiple possible outcomes. The examples involving animals, linear scores, exponential functions, and probability distributions make the concept easier to understand.
ReplyDeleteThe discussion is particularly relevant to Machine Learning Training, where activation functions and classification techniques are important concepts. The article also shows how a model can compare scores for different classes and produce probabilities whose total is one.
The explanation of model predictions, input features, scores, and probability distributions can also be useful for Machine Learning Projects For Final Year. Such projects can use classification models where the output represents probabilities across multiple classes.
ReplyDeleteThe mathematical transformation described here also connects with AI Tools Training, particularly when AI applications use machine learning models to interpret and classify data.
ReplyDeleteDeep learning concepts such as neural networks, classification, and model prediction make this topic relevant to Deep Learning Projects For Final Year, particularly for projects involving neural-network-based classification and prediction.
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