Gradient Descent
The optimization algorithm used to train neural networks by iteratively adjusting weights to minimize the loss function.
In-Depth Explanation
Gradient descent is the core optimization algorithm that trains neural networks. It iteratively adjusts model weights in the direction that reduces error, like finding the lowest point in a landscape by always walking downhill.
How gradient descent works:
- Calculate loss (error) for current weights
- Compute gradients (slope) via backpropagation
- Update weights: new = old - learning_rate × gradient
- Repeat until convergence
Variants of gradient descent:
- Batch: Use all training data per update (slow, stable)
- Stochastic (SGD): Use single example per update (noisy, fast)
- Mini-batch: Use small batches (balance of both)
- Momentum: Add velocity to escape local minima
- Adam: Adaptive learning rates per parameter
Key hyperparameters:
- Learning rate: Step size (critical to tune)
- Batch size: Examples per update
- Momentum: Weight of previous updates
Challenges:
- Local minima and saddle points
- Learning rate selection
- Training instability
Business Context
Understanding gradient descent helps explain why AI training requires significant compute and why learning rates matter.
How Clever Ops Uses This
We configure optimization settings appropriately for fine-tuning projects, balancing training speed with model quality for business clients.
Example Use Case
"The algorithm adjusts model weights step by step to reduce prediction errors - like finding the bottom of a valley by always walking downhill."