A Complete Guide to Neural Network Optimizers
3 months ago
- #neural-networks
- #machine-learning
- #optimization
- Neural network training is an optimization problem aiming to minimize the loss function by finding the best weights.
- Optimization algorithms help navigate complex loss landscapes with valleys, plateaus, and saddle points.
- Seven key optimizers are discussed: SGD, Momentum, Nesterov Momentum, AdaGrad, RMSProp, Adam, and AdamW.
- SGD is simple but can oscillate and converge slowly due to a fixed learning rate.
- Momentum reduces oscillations by accumulating gradients, speeding up convergence in consistent directions.
- Nesterov Momentum improves upon Momentum by anticipating future gradient directions for more precise updates.
- AdaGrad adapts learning rates per parameter, useful for sparse gradients but suffers from rapid learning rate decay.
- RMSProp stabilizes learning rates using exponentially weighted averages of past squared gradients.
- Adam combines Momentum and RMSProp, offering adaptive learning rates and momentum with bias correction.
- AdamW decouples weight decay from gradient updates, improving generalization over Adam.
- Optimizer choice depends on problem characteristics like dataset size, gradient sparsity, and computational budget.
- Adam and AdamW are popular for their robust performance, but SGD with Momentum remains competitive in certain tasks.