Building on Part 2, this tutorial demonstrates how to optimize a neural network's bias parameter using gradient descent. The author plots Sum of Squared Residuals (SSR) against bias values, showing a curve where the lowest point represents the optimal setting. Instead of manual trial-and-error, the piece explains how to calculate the derivative of SSR with respect to the bias, b3, using the chain rule.

The core technique involves plugging the calculated slope into the gradient descent equation. Starting with an initial bias of 0 and a learning rate of 0.1, the author walks through the iterative process. Each step adjusts the bias to reduce the error, shifting predictions closer to the actual data points of 0, 1, and 0. The calculations show the slope decreasing from -15.7 to -6.26 as the model improves.

After several iterations, the process converges when the step size approaches zero. The final optimal bias value of 2.61 minimizes the SSR, aligning the model's predictions with the target outputs. This method provides a systematic, mathematical approach to parameter tuning, a fundamental concept for training any machine learning model. The author provides a Colab notebook for hands-on experimentation.