What is Learning rate?Learning rate kya hai?

🧸 Think of it likeAise samjho

Like the size of your steps when looking for something in the dark: too big and you keep overshooting, too small and it takes forever.Andhere mein kuch dhoondhte waqt kadmon ke size jaisa: bahut bade toh aage nikal jaate ho, bahut chhote toh bahut der lagti hai.

📘 ExplainedVistaar se

Too high and training jumps around or diverges; too low and it's painfully slow or gets stuck. Schedules (warm up, then decay) and optimisers like Adam help. It's usually the most important hyperparameter to tune.Bahut zyaada to training uchhalti ya bigad jaati hai; bahut kam to bahut slow ya atak jaati hai. Schedules (warm up, phir decay) aur Adam jaise optimisers madad karte hain. Aam taur pe tune karne ka sabse zaroori hyperparameter yahi hai.

💡 Real exampleAsli example

Loss bouncing up and down usually means the learning rate is too high.Loss ka upar neeche uchhalna aksar matlab learning rate bahut zyaada 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

• 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.
• HyperparameterA setting you choose before training, like learning rate or tree depth, rather than something the model learns.Training se pehle chuni setting, jaise learning rate ya tree depth, jo model khud nahi seekhta.