Welcome to the Calculus repository! This collection is designed to help you understand and apply various calculus concepts that are essential for machine learning, data science, and beyond. Whether you are a student, a researcher, or just curious about calculus, you'll find valuable resources here.
Learn the basics of derivatives, including rules and applications. Understand how derivatives are used to measure rates of change and how they are foundational to other calculus concepts.
Dive into optimization techniques that are crucial for finding the best solutions in various mathematical models. This section covers different methods and their applications.
Explore the concept of gradients and how they are used in multivariable calculus. Gradients are key to understanding how functions change and are widely used in optimization problems.
Understand gradient descent, one of the most popular optimization algorithms. Learn how it iteratively adjusts parameters to minimize a function, commonly used in training machine learning models.
Discover how optimization techniques are applied in the context of neural networks. This section covers methods used to train models and improve their performance.
Study Newton's method for finding successively better approximations to the roots (or zeroes) of a real-valued function. This powerful technique is used in various scientific and engineering applications.
To get started with the content in this repository, you can clone the repository to your local machine using the following command:
git clone https://github.com/Samarth4023/Calculus.gitNavigate to the respective directories to explore the concepts and examples provided.
I welcome contributions from the community! If you have any improvements, additional content, or suggestions, please feel free to submit a pull request or open an issue.
If you have any questions or need further assistance, feel free to reach out:
At my profile / Readme.md :)
Thank you for visiting this repository.
Happy learning! 🌟