🧠What REALLY Happens Inside a Neuron? | ⚡न्यूरॉन के अंदर असल में क्या होता है? I (AKTU Students)
🎓 Hey friends! Today, we tackle a simple yet crucial question: “Why should we learn how neurons work?”
1. Students often get confused while studying Neural Networks about: What role do inputs and weights play?
2. When does a neuron fire, and when does it not?
3. And why is the ‘activation function’ so vital?
These concepts can feel abstract — just numbers and graphs without any real understanding of what's happening. This often makes Neural Networks seem boring or tough.
🔥 But here’s the exciting news: In this video, we’ll use a super relatable - Picnic Fund Analogy to make everything crystal clear!
"Picture a college-life scenario that you can instantly relate to.
📌 What you’ll uncover in this video:
(i) What exactly is an Activation Function?
(ii) Step, Linear, Sigmoid, Tanh, and ReLU explained as distinct Principals.
(iii) How inputs (students), the summing function (treasurer), and the final decision (Principal) work together just like a neuron.
👥 Storyline:
1. Students (Aman, Ravi, Rajeev, Riya) chip in ₹100, ₹100, ₹100, and ₹200.
2. The Treasurer sums up contributions (Summing Function) → Total ₹500.
3. The principal (Activation Function) reviews the report and decides whether the picnic happens or not.
🏛️ Meet the Principals (Activation Functions):
✔ Step Principal → Strict yes/no
✔ Linear Principal → Directly proportional output
✔ Sigmoid Principal → Smooth probability (0–1)
✔ Tanh Principal → Balanced mood (–1 to +1)
✔ ReLU Principal → Simple & efficient, positive only
🎊 Wrap-up: Activation functions are like Principals — the ultimate authority deciding if the neuron "fires" or not.
👉 After watching this video, you’ll have a clear grasp of how neurons process inputs and make decisions. This foundation will help you tackle tough topics in AI, Machine Learning, and Deep Learning.
👍 Don’t forget to like, share, and subscribe! 💬 Share your thoughts in the comments: Which Principal did you like the most — Step, Sigmoid, Tanh, Linear, or ReLU?
#neuralnetworks
#ActivationFunction
#MachineLearning
#AI
#DeepLearning
#AKTU
Related Topics: • Neural Networks Basics • Perceptron Learning Algorithm • Deep Learning for Beginners
👍 Like the video, share with friends, and subscribe for more student-friendly AI & ML tutorials! 💬 Comment below: Which Principal (Activation Function) is your favorite — Step, Sigmoid, Tanh, Linear, or ReLU?
🎓 Hey friends! Today, we tackle a simple yet crucial question: “Why should we learn how neurons work?”
1. Students often get confused while studying Neural Networks about: What role do inputs and weights play?
2. When does a neuron fire, and when does it not?
3. And why is the ‘activation function’ so vital?
These concepts can feel abstract — just numbers and graphs without any real understanding of what’s happening. This often makes Neural Networks seem boring or tough.
🔥 But here’s the exciting news: In this video, we’ll use a super relatable – Picnic Fund Analogy to make everything crystal clear!
“Picture a college-life scenario that you can instantly relate to.
📌 What you’ll uncover in this video:
(i) What exactly is an Activation Function?
(ii) Step, Linear, Sigmoid, Tanh, and ReLU explained as distinct Principals.
(iii) How inputs (students), the summing function (treasurer), and the final decision (Principal) work together just like a neuron.
👥 Storyline:
1. Students (Aman, Ravi, Rajeev, Riya) chip in ₹100, ₹100, ₹100, and ₹200.
2. The Treasurer sums up contributions (Summing Function) → Total ₹500.
3. The principal (Activation Function) reviews the report and decides whether the picnic happens or not.
🏛️ Meet the Principals (Activation Functions):
✔ Step Principal → Strict yes/no
✔ Linear Principal → Directly proportional output
✔ Sigmoid Principal → Smooth probability (0–1)
✔ Tanh Principal → Balanced mood (–1 to +1)
✔ ReLU Principal → Simple & efficient, positive only
🎊 Wrap-up: Activation functions are like Principals — the ultimate authority deciding if the neuron “fires” or not.
👉 After watching this video, you’ll have a clear grasp of how neurons process inputs and make decisions. This foundation will help you tackle tough topics in AI, Machine Learning, and Deep Learning.
👍 Don’t forget to like, share, and subscribe! 💬 Share your thoughts in the comments: Which Principal did you like the most — Step, Sigmoid, Tanh, Linear, or ReLU?
#neuralnetworks
#ActivationFunction
#MachineLearning
#AI
#DeepLearning
#AKTU
Related Topics: • Neural Networks Basics • Perceptron Learning Algorithm • Deep Learning for Beginners
👍 Like the video, share with friends, and subscribe for more student-friendly AI & ML tutorials! 💬 Comment below: Which Principal (Activation Function) is your favorite — Step, Sigmoid, Tanh, Linear, or ReLU?
