โจ ๐
๐ซ๐จ๐ฆ ๐๐ซ๐ซ๐จ๐ซ ๐ญ๐จ ๐๐ฑ๐๐๐ฅ๐ฅ๐๐ง๐๐ โจ
๐๐จ๐ฐ ๐๐จ๐ฌ๐ฌ ๐
๐ฎ๐ง๐๐ญ๐ข๐จ๐ง๐ฌ ๐๐ก๐๐ฉ๐ ๐๐๐ฎ๐ซ๐๐ฅ ๐๐๐ญ๐ฐ๐จ๐ซ๐ค ๐๐ซ๐๐ข๐ง๐ข๐ง๐ โ
โป ๐๐ก๐๐ญ ๐ข๐ฌ ๐ ๐๐จ๐ฌ๐ฌ ๐
๐ฎ๐ง๐๐ญ๐ข๐จ๐ง?
Imagine youโre teaching a child to throw a ball into a basket. Each time the child throws the ball, you can see how far off the throw was from hitting the target. A loss function is like that measurement โ it tells you how far off the neural networkโs predictions are from the actual answers.
โป ๐๐ก๐ฒ ๐๐จ ๐๐ ๐๐๐๐ ๐ ๐๐จ๐ฌ๐ฌ ๐
๐ฎ๐ง๐๐ญ๐ข๐จ๐ง?
In the world of neural networks, we want our models to make accurate predictions. To achieve this, we need a way to measure how good or bad the modelโs predictions are. This is where the loss function comes in. It provides a numerical value indicating the modelโs prediction error.
โป ๐๐จ๐ฐ ๐๐จ๐๐ฌ ๐๐ญ ๐๐จ๐ซ๐ค?
1. ๐๐๐ค๐ ๐ ๐๐ซ๐๐๐ข๐๐ญ๐ข๐จ๐ง: The neural network takes in some input data and makes a prediction.
2. ๐๐๐ฅ๐๐ฎ๐ฅ๐๐ญ๐ ๐ญ๐ก๐ ๐๐จ๐ฌ๐ฌ: The loss function compares the modelโs prediction to the actual answer and calculates a loss value.
3. ๐๐๐ฃ๐ฎ๐ฌ๐ญ ๐ญ๐ก๐ ๐๐จ๐๐๐ฅ: The neural network uses this loss value to adjust its internal parameters (weights and biases) to improve future predictions. This adjustment process is called training.
โป ๐๐ฒ๐ฉ๐๐ฌ ๐จ๐ ๐๐จ๐ฌ๐ฌ ๐ ๐ฎ๐ง๐๐ญ๐ข๐จ๐ง๐ฌ:
1) ๐๐๐ ๐ซ๐๐ฌ๐ฌ๐ข๐จ๐ง ๐๐ซ๐จ๐๐ฅ๐๐ฆ๐ฌ:
โข ๐๐๐๐ง ๐๐ช๐ฎ๐๐ซ๐๐ ๐๐ซ๐ซ๐จ๐ซ (๐๐๐): Measures the average squared difference between predicted and actual values, heavily penalizing larger errors.
โข ๐๐๐๐ง ๐๐๐ฌ๐จ๐ฅ๐ฎ๐ญ๐ ๐๐ซ๐ซ๐จ๐ซ (๐๐๐): Measures the average absolute difference between predicted and actual values, providing a more robust metric against outliers.
2) ๐๐ข๐ง๐๐ซ๐ฒ ๐๐ฅ๐๐ฌ๐ฌ๐ข๐๐ข๐๐๐ญ๐ข๐จ๐ง ๐๐ซ๐จ๐๐ฅ๐๐ฆ๐ฌ:
โข ๐๐ข๐ง๐๐ซ๐ฒ ๐๐ซ๐จ๐ฌ๐ฌ ๐๐ง๐ญ๐ซ๐จ๐ฉ๐ฒ: Evaluates the difference between the predicted probability and the actual binary class label, penalizing incorrect predictions based on their confidence.
3) ๐๐ฎ๐ฅ๐ญ๐ข๐๐ฅ๐๐ฌ๐ฌ ๐๐ฅ๐๐ฌ๐ฌ๐ข๐๐ข๐๐๐ญ๐ข๐จ๐ง ๐๐ซ๐จ๐๐ฅ๐๐ฆ๐ฌ:
โข ๐๐๐ญ๐๐ ๐จ๐ซ๐ข๐๐๐ฅ ๐๐ซ๐จ๐ฌ๐ฌ ๐๐ง๐ญ๐ซ๐จ๐ฉ๐ฒ: Computes the difference between predicted probabilities and actual class labels for multiple classes, used when class labels are one-hot encoded.
โข ๐๐ฉ๐๐ซ๐ฌ๐ ๐๐๐ญ๐๐ ๐จ๐ซ๐ข๐๐๐ฅ ๐๐ซ๐จ๐ฌ๐ฌ ๐๐ง๐ญ๐ซ๐จ๐ฉ๐ฒ: Similar to categorical cross entropy but used when class labels are provided as integers instead of one-hot encoded vectors.
#๐๐๐ญ๐๐๐ง๐๐ฅ๐ฒ๐ฌ๐ข๐ฌ #DataScience #๐๐๐ญ๐๐๐ง๐ ๐ข๐ง๐๐๐ซ๐ข๐ง๐ #๐๐๐๐ก๐ข๐ง๐๐๐๐๐ซ๐ง๐ข๐ง๐ #๐๐๐๐ฉ๐๐๐๐ซ๐ง๐ข๐ง๐ #LossFunction #๐๐๐ฎ๐ซ๐๐ฅ๐๐๐ญ๐ฐ๐จ๐ซ๐ค #๐๐ข๐ ๐ข๐ญ๐๐ฅ๐๐ซ๐๐ข๐ง #๐๐ซ๐ญ๐ข๐๐ข๐๐ข๐๐ฅ๐๐ง๐ญ๐๐ฅ๐ฅ๐ข๐ ๐๐ง๐๐ #๐๐๐ง๐๐ซ๐๐ญ๐ข๐ฏ๐๐๐ #๐๐๐ซ๐๐๐ฉ๐ญ๐ซ๐จ๐ง #๐๐๐
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