1. Foundations6 lessons
  2. Inside the Model5 lessons
  3. How Learning Works5 lessons

Topic 3 of 3

How Learning Works

Why a model gets better: you'll see how it measures errors, traces improvements backward through its calculation steps, and adjusts its weights step by step.

5lessons in preparation0 of 5 lessons readable

What this topic covers

This topic is about the process that turns an unpracticed computational structure into a useful model. You will trace how training tasks are created, how errors become measurable, and how many small parameter updates gradually improve behavior.

The sequence makes an otherwise hidden cycle tangible: predict, measure the error, calculate each weight’s influence, and adjust the weights deliberately.

Topic 3 of 3

Lessons in this topic

Every lesson stands on its own. In this order, the ideas connect especially clearly.

  1. Text Becomes Many Practice Problems

    In preparation

  2. Forward Pass and Loss: How the Model Measures Its Own Error

    In preparation

  3. Backpropagation and Gradients: How the Model Knows What to Change

    In preparation

  4. Optimization: What Actually Changes the Weights

    In preparation

  5. Batch, Epoch, Step, Token Budget: How to Measure Training Progress

    In preparation