Optimizers: 100 Basic — Advanced Tips and Strategies for Efficient Model Training

Photo by Shubham Dhage on Unsplash

Optimizers play a crucial role in training machine learning models by adjusting the model parameters to minimize the loss function. Here are 100 tips and tricks for working with optimizers:

1. Basics of Optimizers:

1. Understand Optimizer Role: Know that optimizers adjust model parameters to minimize the loss function.
2. Choose Appropriate Optimizer: Different optimizers suit different tasks; experiment to find the most effective one.
3. Learning Rate Exploration: Perform learning rate exploration to find an optimal learning rate for your optimizer.
4. Learning Rate Schedules: Implement learning rate schedules to adaptively adjust the learning rate during training.

2. Gradient Descent Variants:

1. Stochastic Gradient Descent (SGD): Basic optimizer suitable for large datasets.
2. Mini-Batch Gradient Descent: Balance between SGD and full-batch GD for efficiency.
3. Batch Gradient Descent: Computationally intensive but can be effective for small datasets.

3. Adaptive Learning Rate Methods:

1. Adam Optimizer: Popular choice…