What is Training?Training kya hai?

🧸 Think of it likeAise samjho

Like practice sessions where the model looks at many examples and slowly gets better at the task.Practice sessions jaisa jahan model bahut saare examples dekh kar dheere dheere kaam mein behtar hota hai.

📘 ExplainedVistaar se

The algorithm makes predictions on training data, measures the loss, and adjusts parameters to reduce it, over many iterations. Training needs data, compute and evaluation on separate data. Pretraining a large model is very expensive; most teams fine tune or simply use existing models.Algorithm training data pe predictions karta hai, loss naapta hai, aur use kam karne ke liye parameters badalta hai, kai baar. Training ko data, compute aur alag data pe evaluation chahiye. Bade model ki pretraining bahut mehngi hai; zyaadatar teams fine tune karti hain ya seedhe existing models use karti hain.

💡 Real exampleAsli example

Training a churn model on 2 years of customer history takes 10 minutes on a laptop.2 saal ki customer history pe churn model training laptop pe 10 minute leti hai.

📖 Learn it step by stepStep by step seekho

• How machines learn: features, labels and trainingMachines kaise seekhti hain: features, labels aur training
• Training vs inference: how ML runs in productionTraining vs inference: ML production mein kaise chalta hai

🔗 Related termsJude hue shabd

• InferenceUsing a trained model to make predictions on new data.Trained model se naye data pe predictions karna.
• Loss functionA formula that measures how wrong the model's predictions are; training tries to make it small.Formula jo naapta hai model ki predictions kitni galat hain; training ise chhota karne ki koshish karti hai.
• EpochOne full pass of the training algorithm over the entire training dataset.Training algorithm ka poore training dataset pe ek poora chakkar.