/Building-Gradient-Descent-Methods-from-Scratch

Implemented optimization algorithms, including Momentum, AdaGrad, RMSProp, and Adam, from scratch using only NumPy in Python. Implemented the Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimizer and conducted a comparative analysis of its results with those obtained using Adam.

Primary LanguageJupyter NotebookOtherNOASSERTION

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Building Gradient Descent Methods from Scratch

This project implements various optimization algorithms using only NumPy in Python. The implemented algorithms include:

  • Momentum
  • AdaGrad
  • RMSProp
  • Adam

Additionally, the Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimizer is also implemented. The project aims to conduct a comparative analysis of the results obtained from the BFGS optimizer and those obtained from using Adam.

The project involves the following steps:

  • Implementing the optimization algorithms using NumPy.
  • Implementing the BFGS optimizer.
  • Conducting experiments to compare the results obtained from the BFGS optimizer and those obtained from using Adam.
  • Analyzing the results and drawing conclusions.

Through this project, we hope to gain a deeper understanding of optimization algorithms and their performance in various scenarios.