Gradient Descent Optimization
Gradient descent is the optimization algorithm that powers neural network training. It works by computing the gradient (direction of steepest increase) of a loss function and moving parameters in the opposite direction to minimize loss. By iterating this process, the algorithm gradually improves the model's parameters.
What You'll Learn
Prerequisites
Recommended knowledge before starting this visualization
- Basic calculus concepts
- Understanding of functions and derivatives
Interactive Visualization
Gradient Descent Overview
Gradient descent is an optimization algorithm used to minimize a loss function by iteratively moving in the direction of steepest descent. It's the fundamental algorithm that enables neural networks to learn. We'll visualize how it navigates a 2D loss landscape to find the minimum.
∑Mathematics
Parameters that control the optimization process
Learning Rate: 0.1 Iterations: 10 Starting Point: (0, 0)
Neural Network State
Real-time calculation with actual math
Key Takeaways
📉 Loss Minimization
Gradient descent finds the minimum of a loss function by iteratively moving parameters in the direction that reduces loss the most.
📐 Gradient Direction
The gradient points uphill (direction of steepest increase). We move in the opposite direction to go downhill toward the minimum.
🎚️ Learning Rate
The learning rate controls step size. Too large causes overshooting; too small makes learning slow. Finding the right balance is crucial.
🎯 Convergence
As we approach the minimum, gradients become smaller and steps get shorter. This natural slowdown helps us settle at the optimum without overshooting.
🧠 Neural Network Training
This same process is used to train neural networks, but with millions of parameters instead of just two. The loss function measures prediction errors.
Next Steps
Continue your AI learning journey
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