Algorithms for Non-Linear Equations: Solving with Gradient Descent

Algorithms for Non-Linear Equations: Solving with Gradient Descent

💥💥 GET FULL SOURCE CODE AT THIS LINK 👇👇

Learn how to employ gradient descent to find the minimum of non-linear functions representing solution to non-linear equations. this iteration-based optimization technique adjusts parameters to minimize a cost function. Our example uses Python and NumPy for implementation.

Understanding the fundamentals of non-linear equations and their applications, such as regression, neural networks, and optimization problems, is integral to mastering computational data science. After familiarizing yourself with this topic, extending your knowledge base with optimization techniques crucial in developing efficient solutions. Gradient Descent is a powerful optimization technique widely utilized in machine learning for minimizing non-linear functions.

Initially, let us dive into the basics of non-linear equations and gradient descent, along with the essential mathematics. We will then demonstrate the application using Python and NumPy. Step by step, calculate derivatives and iterate through gradient descent's iterations.

Once equipped with this knowledge, further studies may include delving into gradient descent variants, such as Stochastic and Mini-batch Gradient Descent or Quadratic Algorithm.

Additional Resources:
- "Numerical Recipes: The Art of Scientific Computing" by Wiliam H. Press, S.A. Teukolsky, W.T. Vetterling, and Brian P. Flannery
- "Machine Learning: A Probabilistic Perspective" by Kevin P. Murphy

#STEM #Programming #Technology #NonLinearEquations #GradientDescent #Optimization #ComputationalScience #MachineLearning #Python #NumPy

Find this and all other slideshows for free on our website: