Gradient clipping is a technique that limits the magnitude of the gradients during backpropagation. It prevents the gradients from becoming too large and causing numerical overflow or divergence.

Gradient clipping limits the magnitude of the gradients to a predefined threshold so they do not exceed a certain range. This can prevent the model parameters from changing too much or too little ...

Learn how gradient descent really works by building it step by step in Python. No libraries, no shortcuts—just pure math and code made simple.

gradient_descent.py: Python script implementing the Gradient Descent algorithm with a convergence criterion. README.md: This file, providing an overview and instructions for running the project. The ...

Dive deep into Nesterov Accelerated Gradient (NAG) and learn how to implement it from scratch in Python. Perfect for improving optimization techniques in machine learning! 💡🔧 # ...