Little Bits of Artificial Intelligence

1. Imagine a Student Taking a Test

1. Input: The student sees a question (like the input layer of a neural network).
2. Thinking Process: They go through steps in their brain to reach an answer (like the hidden layers doing math with weights and biases).
3. Answer: The student writes the answer (like the network producing an output).
4. Feedback: The teacher marks it wrong and tells the correct answer.
5. Learning: The student realizes where they made a mistake, goes back through their thought process, and adjusts how they think next time.

Neural Network Version of That

1. Forward Pass:
   • Inputs go through the network.
   • It produces an output (a prediction).
2. Compare with the Real Answer:
   • Check how wrong the prediction was (this is called loss or error).
3. Backpropagation (the key idea):
   • The network works backwards from the error.
   • It calculates how much each neuron and weight contributed to the mistake.
4. Update Weights:
   • The network adjusts the weights a little using this info.
   • This makes the future prediction slightly better.

This repeats every time with new examples → and over time the network gets smarter.

Simple Analogy:

It’s like adjusting our aim after every dart throw by figuring out how far off you were and why — so our next throw lands closer to the bullseye.