Name: RDN
Type: User
Company: Technical University of Munich
Bio: Research Interests: Time-Sensitive Networking , 5G || Currently working on IEEE 802.1 standards, AI and ML-based optimization, heuristics, and genetic algorithm
Location: Munich
Blog: https://www.ce.cit.tum.de/en/esi/staff/debnath/
RDN's Projects
500 AI Machine learning Deep learning Computer vision NLP Projects with code
A Deep-Reinforcement Learning Approach for Software-Defined Networking Routing Optimization
Autonomous Navigation Framework
Awesome lists about 5G projects.
A curated list of awesome Machine Learning frameworks, libraries and software.
Awesome machine learning for combinatorial optimization papers.
A curated list of awesome mobile machine learning resources for iOS, Android, and edge devices.
A awesome list about Software Defined Network (SDN)
A curated list of awesome self-supervised methods
Conference List with Deadlines
List of courses taken at Coursera and the solutions of the different courses.
The CQF-based TSN simulator and DRL scheduler
Example project of DDS-TSN integration
DRL on JSSPs
Learning to schedule distributed resources with deep reinforcement learning.
DeepCoord: Self-Learning Network and Service Coordination Using Deep Reinforcement Learning
PPO implementation of the DRL agent used in the paper "Deep Reinforcement Learning meets Graph Neural Networks: exploring a routing optimization use case"
Contains the code related to our paper on scheduling and resource allocation in wireless communications.
this repository is used to reappear thesis《Dynamic scheduling for flexible job shop with new job insertions by deep reinforcement learning》
Deep Reinforcement Learning based Time-Sensitive Network Scheduler
This is the official code of the publised paper 'A Multi-action Deep Reinforcement Learning Framework for Flexible Job-shop Scheduling Problem'
The fastai book, published as Jupyter Notebooks
This is the repository for the collection of Graph-based Deep Learning for Communication Networks.
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)