What is Gradient Descent? Simple Machine Learning Explanation

Описание: Welcome to this simple whiteboard lesson on Gradient Descent — one of the most important ideas in Machine Learning and Deep Learning.

Gradient Descent is how a model learns from mistakes. It checks how wrong its prediction is, computes the direction that reduces error and updates its weights step by step until the loss becomes small.
This video explains the concept visually, shows the learning rule, and gives a simple analogy using a ball rolling down a hill to reach the lowest error point.

By the end of this short class you will understand:
• Why Gradient Descent is needed
• How models reduce loss
• What the update rule means
• How learning rate affects training
• Why almost every AI system uses this process

Perfect for AI beginners, ML students, engineers, developers, data science learners and anyone curious about how neural networks actually learn.
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