Optimizing Gradient Descent for Global Optimization

Beginner

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.

PythonMachine Learning

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

Teacher

labby

Labby

Labby is the LabEx teacher.