What is Activation function?Activation function kya hai?

🧸 Think of it likeAise samjho

Like a doorkeeper for each brain cell: weak signals are stopped at the door, strong ones are let through. That on and off choice lets the network learn bends and curves.Har brain cell ke liye darbaan jaisa: kamzor signal darwaaze pe ruk jaate hain, strong andar jaate hain. Yahi on off ka faisla network ko mod aur curves seekhne deta hai.

📘 ExplainedVistaar se

Each neuron computes a weighted sum of its inputs and passes it through an activation function. Without it, stacking layers would still only produce a straight line relationship. ReLU (keep positives, zero negatives) is the common default in hidden layers; GELU is used in transformers; sigmoid and softmax turn outputs into probabilities.Har neuron apne inputs ka weighted sum nikaalta hai aur use activation function se guzaarta hai. Iske bina layers jodne pe bhi sirf seedhi line wala rishta banta. Hidden layers mein ReLU (positive rakho, negative ko zero) common default hai; transformers mein GELU; sigmoid aur softmax outputs ko probabilities mein badalte hain.

💡 Real exampleAsli example

ReLU turns -3 into 0 and keeps 5 as 5.ReLU -3 ko 0 bana deta hai aur 5 ko 5 hi rehne deta hai.

📖 Learn it step by stepStep by step seekho

• Neural networks: layers that learnNeural networks: layers jo seekhti hain

🔗 Related termsJude hue shabd

• Neural networkA model made of layers of simple connected units (neurons) that learn by adjusting connection weights.Simple jude hue units (neurons) ki layers se bana model jo connection weights badal kar seekhta hai.
• SoftmaxA function that turns a list of scores into probabilities that add up to 1.Function jo scores ki list ko aisi probabilities mein badalta hai jinka jod 1 ho.