Intro to optimization in deep learning: Gradient Descent

Key takeaways:

• Gradient Descent is a fundamental optimization algorithm used to minimize loss functions in deep learning.
• It works by iteratively updating model parameters in the direction that reduces the loss.
• Learning rate plays a critical role—too high can overshoot minima, too low can slow down convergence.
• There are multiple variants: Batch, Stochastic (SGD), and Mini-Batch Gradient Descent, each with trade-offs in speed and stability.
• Techniques like momentum, learning rate decay, and adaptive optimizers (e.g., Adam) enhance gradient descent’s performance.
• Understanding the gradient descent process is essential for building efficient and well-tuned deep learning models.

Learn more about our products

Still looking for an answer?

This textbox defaults to using Markdown to format your answer.

You can type !ref in this text area to quickly search our full set of tutorials, documentation & marketplace offerings and insert the link!