Lecture 21:Â Gradient Descent: The Simple Algorithm for “Learning” by Taking Steps
Series: The Sequentia Lectures: Unlocking the Math of AIPart 3: The AIToolkit: Calculus & OptimizationLecture 21: Gradient Descent: The Simple Algorithm for “Learning” […]






![A clean, three-step infographic diagram explaining how facial recognition might work with linear algebra. Step 1 ('Capture & Vectorize'): On the left, show a simple, stylized icon of a human face. An arrow points from it to a representation of a vector (a list of numbers in brackets like [0.2, -0.9, 0.5,...]). Label this step '1. Face becomes a Vector'. Step 2 ('Compare'): In the center, show the new 'Live Vector' and a 'Stored Vector' from a database icon. An illustration should show a mathematical operation between them, perhaps using a dot • symbol to represent the dot product, resulting in a 'Similarity Score' (e.g., 'Score: 0.98'). Label this step '2. Compare Vectors (Dot Product)'. Step 3 ('Decision'): On the right, show the 'Similarity Score' being compared against a 'Threshold' (e.g., 'Threshold: 0.95'). An arrow points to a final icon of an 'Unlocked' padlock, indicating a successful match. Label this step '3. Unlock if Score > Threshold'. The overall style is modern, minimalist, and educational, with a clean color palette on a neutral background.](https://sequentia.space/wp-content/uploads/2026/02/126.jpg)


