What is Gradient Descent?

What is Gradient Descent?

In deep learning, we use artificial neural networks to solve complex problems, like recognizing objects in images or understanding speech. These neural networks are made up of many interconnected nodes called neurons, arranged in layers.

Now, imagine you're trying to reach the bottom of a valley blindfolded. You can feel the slope of the ground beneath your feet, but you can't see where you're going. Gradient descent is like a strategy for finding the quickest way down to the valley floor.

Here's how it works:

  1. You start at a random point on the valley slopes.

  2. You feel the slope beneath your feet and take a step in the direction that feels like it's going downhill.

  3. You keep taking steps in the direction that feels like it's going downhill, adjusting your direction as needed based on the new slope you feel.

  4. You continue this process until you reach the bottom of the valley.

In deep learning, the "valley" represents the error or loss function that we're trying to minimize. The goal is to find the set of weights (or values) for the connections between neurons that gives us the smallest possible error.

Gradient descent is the algorithm that helps us navigate this high-dimensional "valley" by adjusting the weights in the neural network in small steps, iteratively moving in the direction that reduces the error the most. It's like taking those small steps downhill until you can't go any further and have (hopefully) found the bottom of the valley, which is the point of minimum error.

The key thing to remember is that gradient descent is an iterative process that slowly nudges the weights in the right direction, rather than trying to find the perfect solution in one shot.

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