Deep Learning with Yacine on MSN

Nesterov accelerated gradient (NAG) from scratch in Python – step-by-step tutorial

Dive deep into Nesterov Accelerated Gradient (NAG) and learn how to implement it from scratch in Python. Perfect for ...
Deep Learning with Yacine on MSN

Create a perceptron from scratch in Python – step by step tutorial

Learn how to build a perceptron from scratch in Python! This tutorial covers the theory, coding, and practical examples, helping you understand the foundations of neural networks and machine learning.
InfoWorld

AI and machine learning outside of Python

In some ways, Java was the key language for machine learning and AI before Python stole its crown. Important pieces of the data science ecosystem, like Apache Spark, started out in the Java universe.
IEEE

sQUlearn: A Python Library for Quantum Machine Learning [Focus: Quantum Software and Its Engineering]

Abstract: sQUlearn introduces a user-friendly, noisy intermediate-scale quantum (NISQ)-ready Python library for quantum machine learning (QML), designed for seamless integration with classical machine ...
C&EN

dpdata: A Scalable Python Toolkit for Atomistic Machine Learning Data Sets

School of Artificial Intelligence and Data Science, Unversity of Science and Technology of China, Hefei 230026, P. R. China Suzhou Institute for Advanced Research, University of Science and Technology ...
C&EN

Python Library for Monte Carlo Simulations with Ab Initio and Machine-Learned Interatomic Potentials

Dave C. Swalm School of Chemical Engineering and Center for Advanced Vehicular Systems, Mississippi State University, Mississippi State, Mississippi 39762, United States Department of Chemical and ...
tech2geek

Scikit-learn: The Essential Machine Learning Library for Python

If you’re learning machine learning with Python, chances are you’ll come across Scikit-learn. Often described as “Machine Learning in Python,” Scikit-learn is one of the most widely used open-source ...
IEEE

Failure management in optical networks with ML: a tutorial on applications, challenges, and pitfalls [Invited]

Abstract: This tutorial identifies and discusses the main design choices and challenges arising in the application of machine learning (ML) to optical network failure management (ONFM), including ...