A repository to prepare you for your machine learning interview, involving most of the questions asked by all the tech giants and local companies. Do this to Ace your Machine Learning Engineer Interviews.
MLOps for DevOps Engineers - A hands-on, project-based guide to Machine Learning Operations.
Key parameters and specifications
Key parameters and specifications
Parameter
Value
Language
Python
Category
Tutorials
Subcategory
ML/AI fundamentals
Keywords
Physics
Stars
679
Forks
244
Contributors
100
Created date
2019-10-09
Updated date
2026-10-16
Stars in 1 day
-1
Stars in 7 days
-1
Rank
19,132
Previous rank
14,917
Rank change
-4,215
7-day stars rank
17,727
Previous 7-day stars rank
17,681
7-day stars rank change
-46
Total stars rank
15,236
Previous total stars rank
15,193
Total stars rank change
-43
Country
Canada
README summary
These demos showcase various applications and techniques in quantum machine learning (QML), quantum chemistry, and quantum computing, ranging from introductory concepts to advanced algorithms. Explore the full collection of PennyLane demos, each available for download as a Jupyter notebook or Python script. Table of Contents Contributing to Demos CLI Tool Dependency Management Support License Support **Source Code:** **Issue Tracker:** If you encounter any issues, have questions, or wish to suggest improvements, please report them on our GitHub issue tracker. We
PennyLaneAI/demos has 679 stars, uses Python and belongs to Tutorials.
Which repository is closest in rank?
PennyLaneAI/demos is compared with claude-code-chinese/claude-code-guide, ranked 19,129 with 683 stars.
README
PennyLane Demonstrations
This repository hosts a collection of demonstrations built with PennyLane, a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Built by researchers, for research. These demos showcase various applications and techniques in quantum machine learning (QML), quantum chemistry, and quantum computing, ranging from introductory concepts to advanced algorithms.
Explore the full collection of PennyLane demos, each available for download as a Jupyter notebook or Python script.
