What is Dropout?Dropout kya hai?

🧸 Think of it likeAise samjho

Like a team practising with random players sitting out, so everyone learns every role and nobody depends on one star.Jaise team random players ko bitha kar practice kare, taaki sab har role seekhein aur koi ek star pe depend na rahe.

📘 ExplainedVistaar se

Dropout is a regularisation method: with, say, 10% probability each neuron's output is set to zero in each training step. This forces redundant, robust patterns and reduces overfitting. It's turned off when the model is used for predictions.Dropout regularisation ka tarika hai: maan lo 10% probability se har training step mein har neuron ka output zero kar diya jaata hai. Isse network mazboot, dohraaye patterns seekhta hai aur overfitting kam hoti hai. Predictions ke time ise band kar dete hain.

💡 Real exampleAsli example

Like a team practising with random players sitting out, so everyone learns every role.Jaise team random khiladiyon ko bitha kar practice kare, taaki sab har role seekhein.

📖 Learn it step by stepStep by step seekho

• Neural networks: layers that learnNeural networks: layers jo seekhti hain
• Train/test split and overfittingTrain/test split aur overfitting

🔗 Related termsJude hue shabd

• RegularisationTechniques that keep a model simpler so it generalises better and overfits less.Tarike jo model ko simple rakhte hain taaki woh behtar generalise kare aur kam overfit ho.
• OverfittingWhen a model memorises its training data, including noise, and does badly on new data.Jab model training data ko, noise samet, ratt leta hai aur naye data pe kharab karta hai.