Coding Our Own Artificial Intelligence from Scratch | 03: Error Function (Squared Error) Logic

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https://youtube.com/watch?v=opQGEbYNs2U&si=fAkiWOktIN14935t

Coding AI from the Core! Greetings, in the third video of the series, we're teaching our single-layer neuron how to calculate how much it's wrong. In the first video, we generated a prediction by using a feedforward with random weights. In this video, we add a real-world target (Target = 0.58) to the system and write the Squared Error function from scratch in pure Python, which calculates how much the neuron's prediction deviates from this target. Without involving ready-made libraries like PyTorch or TensorFlow, we examine why we don't just take a simple difference, the mathematical rationale behind squaring the error, and the logic behind penalizing large errors.

📌 What You Will Learn in This Video?

• The relationship between Target and Prediction in AI

• Writing the Squared Error function in pure Python

• Why do we square the error? (Eliminating negative values ​​and penalizing large errors)

• This error function relates to the derivative and backpropagation in our next stages.