What is Loss function?Loss function kya hai?

🧸 Think of it likeAise samjho

Like the 'how far off was I?' score after each guess. Training keeps adjusting to make that score smaller.Har guess ke baad 'kitna galat tha?' wala score. Training use chhota karne ke liye adjust karti rehti hai.

📘 ExplainedVistaar se

Regression often uses mean squared error; classification uses cross entropy. The loss must be differentiable so gradients can be computed. The loss you optimise isn't always the business metric you care about, so evaluate with both.Regression aksar mean squared error use karta hai; classification cross entropy. Loss differentiable hona chahiye taaki gradients nikal sakein. Jo loss optimise karte ho woh hamesha woh business metric nahi jiski parwah hai, isliye dono se evaluate karo.

💡 Real exampleAsli example

Predicting ₹48 lakh for a ₹50 lakh flat gives a small squared error loss.₹50 lakh ke flat ke liye ₹48 lakh predict karna chhota squared error loss deta hai.

📖 Learn it step by stepStep by step seekho

• Gradients and gradient descent: how models improveGradients aur gradient descent: models behtar kaise hote hain

🔗 Related termsJude hue shabd

• Cross entropyThe standard loss function for classification: it punishes confident wrong answers heavily.Classification ka standard loss function: confident galat jawaabon ko bhaari saza deta hai.
• Gradient descentThe training method that repeatedly adjusts weights in small steps to reduce error.Training ka tarika jo error kam karne ke liye weights ko baar baar chhote kadmon mein badalta hai.