Introduction
In this project, you will learn how to optimize the gradient descent algorithm to overcome the challenge of local optimal points. The gradient descent algorithm is a widely used optimization technique in machine learning and deep learning, but it can sometimes get trapped in local optimal points, preventing it from finding the global optimal solution.
🎯 Tasks
In this project, you will learn:
- How to understand the gradient descent algorithm and the challenges it faces with local optimal points
- How to implement an optimized gradient descent algorithm that can skip local optimal points and arrive at the global optimal point
- How to use techniques such as dynamic learning rate adjustment, momentum, and other optimization methods to improve the performance of the gradient descent algorithm
🏆 Achievements
After completing this project, you will be able to:
- Analyze the behavior of the gradient descent algorithm and identify its limitations
- Design and implement optimization strategies to improve the performance of the gradient descent algorithm
- Apply your knowledge of optimization techniques to solve real-world problems in machine learning and deep learning
