Day 26 of 30Artificial Intelligence
🕸️Neural Networks — The Idea
🎯 Today's objective
Grasp how neural networks learn, conceptually.
📖 Key concepts
- Neurons combine inputs × weights + bias
- Activation adds non-linearity
- Layers stack to learn complex patterns
- Training adjusts weights to reduce error (gradient descent)
💻 Try this code
# A single neuron in plain Python (concept)
def neuron(inputs, weights, bias):
total = sum(i*w for i, w in zip(inputs, weights)) + bias
return 1 if total > 0 else 0 # simple activation
print(neuron([1, 0], [0.6, 0.6], -0.5))👥 Learn it your way
🧒 Young Explorers
A neural net is a team of tiny decision-makers ("neurons") voting together.
Your task
Tweak the weights/bias above until the neuron acts like an AND gate.
💼 Professionals
Understand forward pass, loss, backprop and gradient descent at a conceptual level.
Your task
Explain in 5 lines how lowering "loss" changes the weights during training.
🤖 AI connection
Stack millions of these neurons and you get the deep networks behind image recognition, ChatGPT, and self-driving cars.
✅ Quick check
What do weights control?
How strongly each input influences the neuron’s output.
What does training adjust?
The weights (and biases), to reduce error.