Скачать или смотреть AI Loss Functions Explained: TensorFlow Playground & PyTorch Walkthrough

In Episode 2 of The Learning Model, we’re moving from the "atoms" of AI (Tensors) to the "motivation" behind them. If Tensors are the building blocks, the Loss Function is the scorecard that tells the AI if it’s winning or losing.

Today, I’m demystifying the math by using two different perspectives:

The Visual Lab: We dive into the TensorFlow Playground to see how a model's "opinion" (that background glow) actually shifts as the loss drops.

The Code Walkthrough: I’m opening up my own PyTorch code to show you the calc_loss function. We’ll look at why subtracting the "Truth" from the "Prediction" is the key to everything, and why I center my initial coefficients at zero to keep the model unbiased.

AI can feel like a black box, but when you realize it’s just a game of "Hot or Cold" guided by a single number, it all starts to click.

Topics covered in this episode:

What is a Loss Function? (The Scorecard Analogy)

Visualizing Classification and Noise in TensorFlow Playground.

Breaking down Mean Absolute Error (MAE) in Python.

Why we use torch.abs() and .mean().

Setting a fair starting line with unbiased initialization.

If you’re learning AI from scratch like I am, hit subscribe and let’s figure this out together!
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