Deep Learning with Yacine on MSN

Backpropagation from scratch in Python – step by step neural network tutorial

Learn how backpropagation works by building it from scratch in Python! This tutorial explains the math, logic, and coding behind training a neural network, helping you truly understand how deep ...
IEEE

Distributed Optimization Methods for Multi-Robot Systems: Part 1—A Tutorial [Tutorial]

Abstract: Distributed optimization provides a framework for deriving distributed algorithms for a variety of multi-robot problems. This tutorial constitutes the first part of a two-part series on ...
Deep Learning with Yacine on MSN

Gradient descent from scratch in Python – step by step tutorial

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

GeostatsPy Python Package: Open-source Spatial Data Analytics and Geostatistics

Pyrcz, M.J., Jo. H., Kupenko, A., Liu, W., Gigliotti, A.E., Salomaki, T., and Santos, J., 2021, GeostatsPy Python Package: Open-source Spatial Data Analytics and ...
IEEE

Robust Explainability: A tutorial on gradient-based attribution methods for deep neural networks

Abstract: The rise in deep neural networks (DNNs) has led to increased interest in explaining their predictions. While many methods for this exist, there is currently no consensus on how to evaluate ...
GitHub

Python implementation of Krotov's method for quantum optimal control.

This implementation follows the original implementation in the QDYN Fortran library. The krotov package is built on top of QuTiP. Development happens on Github. You can read the full documentation ...