PyTorch Basics | Optimizers Theory | Part Two

Today, we’ll be diving into the world of gradient descent optimization algorithms in PyTorch. In Part One, we covered the basics of optimizers and their role in updating the parameters of a neural network during the training process. In this article, we’ll explore three popular optimization algorithms – Gradient Descent with Momentum, RMSProp, and Adam – and understand how they work.

Gradient Descent with Momentum

Gradient Descent with Momentum is a variant of the standard gradient descent algorithm that aims to accelerate convergence and dampen oscillations in the parameter updates. It achieves this by introducing a momentum term that accumulates gradient updates over time. This helps the optimizer to maintain a more consistent direction and speed up convergence in the presence of shallow, curvy or plateau regions in the loss landscape.

RMSProp

RMSProp, which stands for Root Mean Square Propagation, is another popular optimization algorithm that adapts the learning rate for each parameter based on the magnitude of recent gradients. It maintains a running average of squared gradients and divides the current gradient by the square root of this average to scale the updates. This helps to mitigate the exploding and vanishing gradient problem and enables better convergence in cases of highly non-convex loss functions.

Adam

Adam, short for Adaptive Moment Estimation, combines the benefits of momentum and RMSProp into a single algorithm. It leverages the momentum term to accumulate history of gradients and the RMSProp approach to adapt learning rates for each parameter individually. Additionally, it introduces bias correction to account for the initial bias towards zero in the moment estimates. Adam has been widely adopted in deep learning due to its robust performance across a wide range of applications and datasets.

These are just a few of the many optimization algorithms available in PyTorch, each with its own unique strengths and weaknesses. As a machine learning practitioner, it’s important to understand the principles behind these algorithms and experiment with different optimizers to find the most suitable one for your specific task.

That wraps up our exploration of gradient descent optimization algorithms in PyTorch. In the next part, we’ll delve into advanced techniques for fine-tuning optimizers and optimizing hyperparameters for better model performance. Stay tuned!