Welcome to the repository dedicated to numerical functions that aid in optimizing artificial intelligence models. Implemented in Python, these functions are categorized based on their functionality, simplifying the search for the specific task at hand.
📢 Call to Collaborators: Are you intrigued by the intersection of numerical methods and AI optimization? We invite passionate individuals to contribute! Find out how to get started here.
🌌 Repository Vision
- Diverse Optimization Techniques: This repository focuses on a variety of numerical methods essential for optimizing AI models, from differentiation to root finding.
- Practical Implementations: Each function is implemented to be modular and easily integrated into various AI projects, offering practical solutions for complex optimization challenges.
- Educational Perspective: Alongside code implementations, the repository provides extensive mathematical documentation, enhancing understanding and learning.
Here are some recommended resources to deepen your understanding of numerical optimization:
| Course/Resource | Provider/Platform |
|---|---|
| Root Finding by Oscar Veliz | YouTube |
| Convex Optimization by Stephen Boyd (Stanford University) | YouTube |
| Numerical Optimization by Jorge Nocedal and Stephen J. Wright | Online Book |
As enthusiasts of mathematics and computer science, the complex world of AI optimization captured our interest. This journey through numerical optimization is not just about code, but about understanding and harnessing mathematical principles to solve real-world problems.
| Name | GitHub Profile |
|---|---|
| Saeed Ahmad | saeedahmadicp |
| Izhar Ali | ali-izhar |
This repository is licensed under the MIT License.