Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single ...

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.

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! 💡🔧 # ...

The outer objective function is a classical strongly convex function which may not be smooth. Motivated by the smoothing approaches, we modify the classical bi-level gradient sequential averaging ...

Gradient variance errors in gradient-based search methods are largely mitigated using momentum, however the bias gradient errors may fail the numerical search methods in reaching the true optimum. We ...

Although distributional RL has been investigated widely in value-based RL methods, very few policy-gradient methods take advantage of distributional RL. To bridge this research gap, we propose a ...

Meet neograd, a newly released deep learning framework developed from scratch using Python and NumPy. This framework aims to simplify the understanding of core concepts in deep learning, such as ...